Applied Artificial Intelligence Technology for Hormone Therapy Treatment

ABSTRACT

Disclosed herein are a number of techniques that systematically integrate a person&#39;s biochemical, symptomatic, and genetic status to generate recommended hormone therapy treatment prescriptions.

CROSS-REFERENCE AND PRIORITY CLAIM TO RELATED PATENT APPLICATIONS

This patent application is a continuation of U.S. patent applicationSer. No. 16/599,697, filed Oct. 11, 2019, and entitled “AppliedArtificial Intelligence Technology for Hormone Therapy Treatment”, nowU.S. Pat. No. ______, where the '697 patent application (1) claimspriority to U.S. provisional patent application Ser. No. 62/745,756,filed Oct. 15, 2018, and entitled “Applied Artificial IntelligenceTechnology for Hormone Therapy Treatment”, and (2) is a continuation ofPCT patent application PCT/US2019/055677, designating the US, filed Oct.10, 2019, and entitled “Applied Artificial Intelligence Technology forHormone Therapy Treatment”, where the PCT/US2019/055677 PCT patentapplication claims priority to U.S. provisional patent application Ser.No. 62/745,756, filed Oct. 15, 2018, and entitled “Applied ArtificialIntelligence Technology for Hormone Therapy Treatment”, the entiredisclosures of each of which are incorporated herein by reference.

INTRODUCTION

Many people suffer from health complications that arise from hormonedeficiencies, particularly people at older ages. The inventor believesthat conventional medical treatments which aim to alleviate the adverseeffects of hormone deficiencies heavily rely on highly imprecise, “ruleof thumb” decision-making by doctors.

For example, FIG. 1A shows an example process flow for conventionalhormone therapy treatment using FDA-approved drugs. At step 100, thedoctor asks the patient about symptoms that are associated with ahormone deficiency. At step 102, the doctor applies his or her knowledgeand experience to decide on an appropriate FDA-approved hormone therapytreatment for the patient in view of the patient's responses to thedoctor's questions about symptoms. This treatment will take the form ofsome formulation of an FDA-approved hormone therapy treatment (e.g., anidentification of an FDA-approved hormone drug or an identification ofan FDA-approved mix of multiple hormone drugs) and a dosage for thatformulation (e.g., how much of the formulation to administer each day).At step 104, the doctor writes a prescription for the patient inaccordance with the treatment decision made at step 102. The patientthen takes this prescription to a pharmacy to be filled (step 106). Atstep 108, the pharmacy fills the prescription, and the patient thenadministers the hormone therapy treatment using the filled prescription(step 110).

This conventional approach to hormone therapy treatment continues duringfollow-ups between the patient and doctor, as shown by FIG. 1B. During afollow-up consultation after the patient has undergone hormone therapytreatment as prescribed by FIG. 1A, the doctor will then ask the patientabout whether the symptoms have improved or gotten any worse (step 120).Based on the patient's feedback at step 120, the doctor once againapplies his or her knowledge and experience to decide on an appropriatehormone therapy treatment (step 122), which may include an adjustment ofsome sort based on the patient answers to the follow-up questions. Atstep 124, the doctor writes a new hormone therapy treatment prescriptionfor the patient based on the decision at step 122. The patient then getsthe new prescription filled (steps 126 and 128) so that the treatment inaccordance with the new prescription can be administered (step 130).

It is not routine or typical for doctors to measure the patient'shormone levels prior to prescribing FDA-approved hormone therapytreatments to their patients. However, when prescribing bio-identicalhormones as part of a hormone therapy treatment, many doctors do soafter the patient's hormone levels have been measured (typically, via asaliva sample). But, even when prescribing customized hormone therapytreatments for patients after reviewing the patients' measured hormonelevels, the inventor believes that doctors are still routinely usingonly their own general ad hoc knowledge to arrive at a prescribed courseof action.

When these conventional approaches for hormone therapy treatment arerepeated for multiple patients and multiple doctors, the result is thatlarge volumes of patients are being prescribed hormone therapy treatmentin a highly de-centralized, un-systematic manner, which results inthousands and thousands of highly differentiated hormone therapytreatment prescriptions, even to patients in similar situations withrespect to their hormone deficiencies. This is particularly a problemwith customized treatments that arise after hormone measurements becausethe measurement-derived customizations introduce a virtually limitlessnumber of variations in potential hormone therapy treatmentprescriptions. Further still, the inventor believes that many doctorswho prescribe hormone therapy treatments for patients develop their“favorite” hormone formulations that then get tweaked in small fashionswhen the doctors decide on a specific prescription for a given patient.This results in compounding pharmacies filling prescriptions forthousands of different hormone formulations, which increases costs forpatients because of the lack of mass production opportunities forhormone formulations (due to the large number of small variationsbetween different prescribed formulations) while not achievingmeasurable improvements in effectiveness.

The inventor believes that improvements are needed in how hormonetherapy treatments are prescribed and administered. Toward this end, theinventor discloses a number of practical applications of computertechnology that systematically integrate a person's biochemical,symptomatic, and genetic status to generate recommended hormone therapytreatment prescriptions for the person.

To obtain data about a person's biochemical status, the person in anexample embodiment can provide a biological sample (e.g., a bodily fluidsample, such as blood, saliva, serum, plasma sample, and/or urine) sothat measurements of the hormones of interest can be obtained. Forexample, the system can quantify measurements of how much of Hormone 1and Hormone 2 are present in the person's blood. This measurement datacan then represent the person's biochemical status.

To obtain data about a person's symptomatic status, the person in anexample embodiment can interact with an application (such as a mobileapplication or web application) to provide the system with data thatrepresents the extent of symptoms experienced by the person with respectto deficiencies of hormones of interest. For example, if there are 5known symptoms of a deficiency of Hormone 1, the application can promptthe person for input that quantifies the severity of these symptoms asexperienced by the person; and if there are 10 known symptoms of adeficiency of Hormone 2, the application can also prompt the person forinput that quantifies the severity of these symptoms as experienced bythe person. The inputs through the application can then represent theperson's symptomatic status.

To obtain data about a person's genetic status, the person in an exampleembodiment can provide a genetic sample (e.g., a bodily fluid sample,such as blood, saliva, serum, plasma sample, and/or urine; a hairsample, a skin sample, or any other biological sample that can be usedto establish a person's genetic profile) so that the person's geneticprofile can be determined. For some people, this genetic testing mayhave already occurred prior to seeking hormone therapy treatment andtheir genetic profiles may be available electronically prior to seekinghormone therapy treatment. For other people, the genetic testing mayoccur as part of the hormone therapy treatment process. The person'sgenetic profile can then represent the person's genetic status.

The computer technology can then systematically integrate the datarepresenting the person's biochemical, symptomatic, and genetic statususing a new and innovative programmatic function that translates thisdata into a recommended hormone therapy treatment prescription. In adramatic departure from the routine and conventional approaches forhormone therapy treatment where doctors apply ad hoc rules of thumbbased on the patient's reported symptoms (and perhaps measured hormonelevels) to arrive at a course of treatment, this new and innovativeprogrammatic function allows for a normalization of treatment acrosslarge populations of patients (because the same programmatic functionwould be used for large populations of patients) while still allowingfor highly personalized, patient-specific treatments.

In doing so, the computer technology does significantly more than merelyautomate the process flows of FIGS. 1A and 1B. First, the conventionalapproach to prescribing hormone therapy treatments does not rely theperson's genetic makeup. Second, the computer technology leverages newquantified relationships between data values and data elements, wherethese relationships were not considered by doctors using routine andknown treatment decision-making. Third, the computer technology providesa level of systematized normalization that was not possible via thede-centralized decision-making shown by FIGS. 1A and 1B. As such, datacan be more accurately tracked over time and optionally across largepatient populations to permit feedback loops that improve upon theanalysis performed by the computer system and optimize how a person'sbiochemical, symptomatic, and genetic status gets translated into arecommendation of a specific hormone therapy treatment. For at leastthese and other reasons, the computer technology described herein doessignificantly more than merely encode doctors' pre-existing techniques,and it represents a pioneering and ground-breaking new manner offacilitating hormone therapy treatment for patients.

In an example embodiment, the inventor discloses that the computersystem can maintain a data structure that associates a plurality ofdifferent prescriptions of a formulation of first and second hormoneswith a first deficiency factor associated with the first hormone and asecond deficiency factor associated with the second hormone. This datastructure embodies a new and innovative manner of relating differenthormone therapy treatment prescriptions with data representative ofquantified hormone deficiencies. The inventor further discloses a newand innovative programmatic function that can compute a first hormonedeficiency factor value and a second hormone deficiency factor valuethat quantifies the person's hormone deficiencies based on the person'sbiochemical, symptomatic, and genetic status. These computed hormonedeficiency factors are new data points that provide actionableintelligence about the person that go beyond mere diagnosis because thecomputed hormone deficiency factors are scaled in a manner that linksthem in an automatable fashion to different treatment options. Thecomputer system can then select a recommended hormone therapy treatmentprescription for the person by looking up in the data structure whichprescription is associated with the computed first and second hormonefactor values.

In other example embodiments, the analysis performed by the computersystem to recommend hormone therapy prescriptions can also take intoconsideration other data about the person. For example, a person's bodyfat composition characteristic (e.g., a body mass index (BMI), body fatpercentage (BFP), or other measure that is indicative of a person's bodyfat composition) can be used to influence how the computer systemselects a recommended prescription. This also represents a vastimprovement over conventional approaches to hormone therapy treatmentbecause the conventional approaches do not systematically consider suchdata elements. As another example, a patient's medication history can beused to influence how the computer system selects a recommendedprescription. As yet another example, a patient's surgical history canbe used to influence how the computer system selects a recommendedprescription.

Further still, the techniques described herein can be extended toprescriptions of a single hormone or more than 2 hormones. For example,the prescription may include a formulation of 3 or more hormones, andthe programmatic function can also leverage the person's biochemical,symptomatic, and genetic status to arrive at an appropriate recommendedprescription of 3 hormones.

Moreover, techniques for tracking patient data and prescriptions overtime allows for innovative feedback techniques whereby treatments andanalysis can be improved over time. In example embodiments discussedbelow, the hormones of interest are progesterone and estrogen to helptreat women who are undergoing peri-menopause, menopause, or otherconditions affecting and/or affected by their female hormone levels.However, it should be understood that these hormones are just examples;and the technology described herein can be applied to other hormones andother types of patients if desired by a practitioner. In certainembodiments, the hormones of interest are thyroid hormones (e.g.,thyroxine (T4) and/or triiodothyronine (T3)) and/or Thyroid-StimulatingHormone (TSH) and the technology described herein is applied to patientswith thyroid hormone-associated disorders (e.g., hyperthyroidism orhypothyroidism).

These and other features and advantages of the present invention will bedescribed hereinafter to those having ordinary skill in the art.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B show conventional processes for hormone treatmenttherapy.

FIG. 2 depicts an example treatment system in accordance with an exampleembodiment.

FIG. 3 depicts an example computer system for use with the treatmentsystem of FIG. 2 to recommend a hormone therapy treatment prescriptionof first and second hormones.

FIG. 4A depicts an example process flow for execution by a computersystem to recommend hormone therapy treatment prescriptions of first andsecond hormones.

FIGS. 4B and 4C depict examples of how first and second hormonedeficiency factors can be systematically computed based on a person'sbiochemical, symptomatic, and genetic status.

FIG. 5 depicts another example computer system for use with thetreatment system of FIG. 2 to recommend a hormone therapy treatmentprescription of first and second hormones.

FIG. 6A depicts another example process flow for execution by a computersystem to recommend hormone therapy treatment prescriptions of first andsecond hormones.

FIGS. 6B and 6C depict examples of how first and second hormonedeficiency factors can be systematically computed based on a person'sbiochemical, symptomatic, genetic, and medical status.

FIGS. 7A-7F depict examples of data structures that can be used forassociating prescription options with various values of deficiencyfactors for the first and second hormones.

FIG. 8A depicts an example system where a mobile application can be usedto collect symptom experience data from patients.

FIG. 8B depicts an example mobile application for use with FIG. 8A.

FIGS. 8C and 8D depict example user interfaces for the mobileapplication of FIGS. 8A and 8B.

FIG. 8E depicts an example user interface for use by a doctor tointeract with the system.

FIG. 8F depicts an example process flow for the computer system to takeactions in response to doctor input via the user interface of FIG. 8E.

FIG. 9 depicts an example process flow where feedback is used to tracksymptoms over time and make adjustments to prescription recommendations.

FIG. 10A depicts an example computer system for use with the treatmentsystem of FIG. 2 to recommend a hormone therapy treatment prescriptionof first, second, and third hormones.

FIG. 10B depicts an example process flow for execution by a computersystem to recommend hormone therapy treatment prescriptions of first,second, and third hormones.

FIG. 10C depicts an example of how third hormone deficiency factors canbe systematically computed based on a person's biochemical andsymptomatic status.

FIGS. 10D and 10E depict examples of data structures that can be usedfor associating prescription options with various values of deficiencyfactors for the first, second, and third hormones.

FIG. 10F depicts an example user interface for a mobile application tocollect symptom experience data from a patient relating to deficienciesof a third hormone.

FIG. 11 depicts an example dispensing system for a hormone therapytreatment prescription.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

FIG. 2 depicts an example treatment system 200 in accordance with anexample embodiment. The system 200 includes an artificial intelligence(AI) computer system 202 that interacts with one or more patients 204,one or more doctors 206, one or more pharmacies 208, one or morebiological sample assays 210, and one or more genetic sample assays 212to compute a recommended prescription for hormone therapy treatment withrespect to patient 204 based on the patient's biochemical, symptomatic,and genetic status.

The biological sample assay 210 can be administered by a testing companyon a biological sample such as a bodily fluid sample provided by thepatient 204. Examples of suitable bodily fluids that can be used for thesample include blood, saliva, serum, plasma sample, and/or urine. Fromthis assay 210, the hormone measurements for the patient 204 can beobtained (e.g., the level of Hormone 1 in the patient 204 and the levelof Hormone 2 in the patient 204). For purposes of discussion, theexamples discussed below will presume that the measured hormone levelsconstitute measured levels of the subject hormone's in the patient'sblood. In certain embodiments, a subject hormone that is measured willcomprise one or more biologically active forms of the hormone. Incertain embodiments where estrogen levels are determined, one or more ofestrone (E1), estradiol (E2 or 17-beta-estradiol), and/or Estriol (E3)can be measured. Measurements of hormone levels can be obtained by anysuitable method including immunological and mass spectrometry-basedmethods. Various mass spectrometry based methods disclosed in U.S. Pat.Nos. 7,473,560, 8,034,627, 8,916,385, and 9,034,653, each of which isspecifically incorporated herein by reference in their entireties, canbe used to measure hormones. Data representing these hormonemeasurements can then be communicated to the computer system 202.

The genetic sample assay 212 can be administered by a testing company ona genetic sample provided by the patient 204. Examples of suitablegenetic samples can include bodily fluids (e.g., blood, saliva, serum,plasma sample, and/or urine), hair samples, skin samples, and the like.From this assay 212, the genetic profile for the patient 204 can beobtained. An example of genetic information that can be useful forsystem 200 include indicators as to whether the patient's has any genesthat are known (or believed) to affect a person's metabolism ofadministered hormones. Additional examples of genetic information thatcan be useful for system 200 include indicators as to whether thepatient has any genes that show a predisposition for certain types ofcancers (e.g., breast cancer, ovarian cancer, cervical cancer, uterinecancer, and/or other cancers that are metastatic and/or known (orbelieved) to possess steroid-responsive cell types. Data representingthe patient's genetic profile can then be communicated to the computersystem 202. It should be understood that because a patient's geneticprofile is not expected to change over time, the genetic testing viaassay 212 need not be performed contemporaneously with the hormonetherapy treatments. For example, a patient 204 may have a pre-existinggenetic profile that can be communicated to the computer system 202,where this genetic profile was created from an assay 212 long in thepast.

The patient 204 can provide data to the computer system 202, where thisdata quantifies any hormone deficiency symptoms experienced by thepatient 204. An application such as a mobile application and/or a webapplication can be made available to the patient 204 to provide thepatient 204 with an effective mechanism for communicating such symptomdata to the computer system 202. In addition to symptom data, thepatient 204 may also provide the computer system 202 with other patientinformation, such as patient medical data (e.g., fat compositioncharacteristic data such as BMI or BFP, cancer history, medicationhistory, and/or surgical history). In certain embodiments, genetic,medication, or surgical history of a 1^(st) degree relative could beused.

Using the data obtained from the patient 204 and assays 210 and 212, thecomputer system 202 produces a recommended hormone therapy treatmentprescription. This prescription can identify a formulation of a firstand second hormone including their relative concentrations.

The prescription can also identify a dosage for the formulation. Instating that the hormone therapy treatment includes a formulation of twohormones, it should be understood that the hormones in the formulationcan take the form of the subject hormone itself or one or morebiologically active forms thereof, an analog thereof, a precursorthereof, and/or a metabolite thereof. Thus, in an example where theformulation includes estrogen, the formulation may include a mixture ofestradiol and estrone (e.g., a mixture of about 80% estradiol and about20% estriol by weight). The computer system 202 can then communicate itsprescription recommendations to a doctor 206. Upon approval of therecommended prescription by the doctor 206, the computer system 202 canthen place a prescription order with a pharmacy 208.

The computer system 202 can utilize any of a number of electroniccommunication mechanisms for interfacing with the doctor 206 andpharmacy 208. For example, an application such as a mobile applicationand/or web application can be made available to the doctor 206 forreviewing the recommended prescriptions and providingapprovals/rejections thereof. To interface with the pharmacy 208, anelectronic link such as a web services communication link with anelectronic business system maintained by the pharmacy 208 can beemployed.

The pharmacy 208 then fills the ordered prescription, and the patient204 then obtains the filled prescription from the pharmacy 208.Thereafter, the patient 204 can administer the prescribed hormonetherapy treatment. Once that prescribed treatment has been completed,the process can be repeated as may be necessary to continue the hormonetherapy treatment for the patient 204.

Accordingly, the system 200 of FIG. 2 can be put into beneficial use byany of a number of different parties. Patients 204 can interact with thesystem 200 in order to obtain hormone therapy treatments that mayrelieve various symptoms of hormone deficiencies. Doctors 206 caninteract with the system 200 in order to more efficiently provide healthcare services to patients 204. Pharmacies 208 can interact with thesystem 200 in order to increase the volume of prescription fills forpatients and thereby increase revenues. Any testing companies thatperform tests with respect to assays 210 and 212 can interact with thesystem 200 in order to increase the volume of testing they perform andthereby increase revenues. Furthermore, given that effective hormonetherapy treatment via system 200 will likely involve follow-up testingof biological sample assays 210 so that the system can track patient'shormone levels over time and assess whether treatments are working, thesystem 200 can thus serve as a potential source of recurring revenue fortesting companies.

The AI computer system 202 can take the form of one or more servers thatare configured for electronic communication via networks such as theInternet and/or wireless telecommunications networks with the patients204, testing companies, doctors 206, and/or pharmacies 208. Such acomputer system 202 may take the form of a distributed computing systemwith multiple servers that are operatively linked via networkedcommunications to provide the features described herein; for example,cloud-based servers. FIG. 3 depicts an example embodiment for computersystem 202, where the computer system includes a processor 300 andmemory 302 that are operatively linked. It should be understood that theprocessor 300 may include multiple processors that perform theoperations described herein in a distributed manner. It should also beunderstood that memory 302 can be a distributed memory.

Memory 302 can store various data about the patient 204 as discussedabove. For example, memory 302 can store data 310 that represents ameasurement for a first hormone in the patient 204 (as derived fromassay 210). Memory 302 can also store data 312 that represents ameasurement for a second hormone in the patient 204 (as derived fromassay 210). Memory 302 can also store symptom experience data 314 thatrepresents a quantification of any symptoms associated with deficienciesof the first and second hormones by the patient 204. Further still,memory 302 can store genetic profile data 316 for the patient 204 (asderived from assay 212).

Processor 300 can then execute an analysis program 304 thatsystematically integrates data 310, 312, 314, and 316 to compute arecommended hormone therapy treatment prescription 320. Analysis program304 can be embodied by a plurality of processor-executable instructionsthat are resident in a non-transitory computer-readable storage mediumsuch as computer memory. FIG. 4A depicts an example process flow forprogram 304. In this example, the hormone therapy treatment willevaluate deficiencies of Hormone 1 (H1) and Hormone 2 (H2). Withreference to FIG. 4A, at step 400, the processor computes an H1deficiency factor (H1DF) for Patient X based on Patient X's H1measurement (see data 310), H1 deficiency symptom data (see data 314),and genetic profile data (see data 316). At step 402, the processorcomputes an H2 deficiency factor (H2DF) for Patient X based on PatientX's H2 measurement (see data 312), H2 deficiency symptom data (see data314), and genetic profile data (see data 316). Examples of techniquesthat can be used for computing H1DF and H2DF are discussed below.

The computer system 202 can also maintain a data structure 410 in memory302, where this data structure 410 associates different options forrecommended prescriptions of H1 and H2 with corresponding values forH1DF and H2DF. For example, this data structure 410 can take the form ofa lookup table where different prescriptions of H1 and H2 (e.g., Rx1with a particular formulation and dosage of H1 and H2, Rx2 with aparticular different formulation and dosage of H1 and H2, etc. throughRxN) are linked to associated values of H1DF and H2DF. Using thesecomputed H1DF and H2DF values, the processor can select the prescriptionthat is to be recommended for the patient (see Step 404 in FIG. 4A).While data structure 410 is described as a look-up table such as a gridin example embodiments below, it should be understood that other typesof data structures such as arrays, linked lists, or any other datastructure suitable for associating prescription recommendations withH1DF and H2DF values that can be used by computer system 202.

FIG. 4B shows examples of functions that can be used by program 304 tocompute H1DF and H2DF. H1DF can be computed as function of an H1Measured Deficiency Factor, an H1 Deficiency Symptom Experience Factor,and a Genetic Metabolism Factor. For example, H1DF can be computed asthe product of the H1 Measured Deficiency Factor, the H1 DeficiencySymptom Experience Factor, and the Genetic Metabolism Factor. H2DF canbe computed as function of an H2 Measured Deficiency Factor, an H2Deficiency Symptom Experience Factor, and the Genetic Metabolism Factor.For example, H2DF can be computed as the product of the H2 MeasuredDeficiency Factor, the H2 Deficiency Symptom Experience Factor, and theGenetic Metabolism Factor.

In an example embodiment, the H1 Measured Deficiency Factor (H1MDF) canbe computed according to an inverse relationship with the measured levelof H1 for the subject patient from assay 210. Thus, in an exampleembodiment, H1MDF can be computed as:

$\begin{matrix}{{H\; 1{MDF}} = \frac{1}{\left\lbrack {H\; 1M} \right\rbrack}} & {{Equation}\mspace{14mu} (1)}\end{matrix}$

where HIM represents the measured level of H1 from assay 210. The valuefor HIM can be included as part of data 310 (see FIG. 3).

In an example embodiment, the H1 Deficiency Symptom Experience Factor(HIDSEF) can be computed as an aggregation of severity data experiencedby the subject patient for different symptoms of an H1 deficiency. Thus,in an example embodiment, HIDSEF can be computed as:

H1DSEF=Σ_(i=1) ^(N)(H1_(S))_(i)  Equation (2)

where N represents the total number of different symptoms associatedwith an H1 deficiency, and where (H1_(S))_(i) represents theuser-specified severity value for symptom i associated with an H1deficiency. The values for (H1_(S))_(i) can be included as part of data314 (see FIG. 3).

In an example embodiment, the Genetic Metabolism Factor (GMF) can becomputed according a function that operates to scale the other factorsbased on the presence of certain genes linked to an effect on steroidhormone metabolism in humans. For example, steroid hormones, includingthe sex steroids, are metabolized primarily by the Cytochrome P450(CP450) family of genes, primarily CYP-1A1 and CYP-1A2 and to a lesserextent CYP-1B1.

Polymorphisms of these genes can dramatically affect (increase ordecrease) a person's metabolism of endogenous and exogenous hormones.Accordingly, if the patient's genetic profile indicates the presence ofan allele of the CP450 family of genes in the patient associated withmore rapid metabolism (i.e., catabolism) of one or more hormones, thenGMF will upwardly adjust H1DF and H2DF. Thus, in an example embodiment,GMF can be computed as:

GMF=1+(W1(CYP-1A1)+W2(CYP-1A2)+W3(CYP-1B1))  Equation(3)

where CYP-1A1 serves as a presence indicator (e.g., the value will be 0if not present and 1 if present) for the patient with respect to anallelic status of CYP-1A1 in the patient, where CYP-1A2 serves as apresence indicator for the patient with respect to an allelic status ofCYP-1A2 in the patient, and where CYP-1B1 serves as a presence indicatorfor the patient with respect to an allelic status of CYP-1B1 in thepatient. The values for CYP-1A1, CYP-1A2, and CYP-1B1 can be included aspart of data 316 (see FIG. 3). Furthermore, the values for W1, W2, andW3 can serve as weights that control how much of an effect the variousgene indicators will have on GMF. W1 can define a weight for the CYP-1A1presence indicator, W2 can define a weight for the CYP-1A2 presenceindicator, and W3 can define a weight for the CYP-1B1 presenceindicator. Non-limiting examples of CYP1A1, CYP1A2, and CYP1B1 allelesassociated with more rapid metabolism of one or more hormones includethose set forth in the National Center for Biotechnology Information(NCBI) world wide web site “ncbi.nlm.nih.gov” and the PharmacogeneVariation (PharmVar) Consortium website on the world wide web at“pharmvar.org” (Gaedigk et al. Clin. Pharm. & Thera. November 2017 PMID:29134625doi:10.1002/cpt.910). Since identification of CYP1A1 (NCBI GeneID: 1543), CYP1A2 (NCBI Gene ID: 1544), and CYP1B1 (NCBI Gene ID: 1545)alleles associated with more rapid metabolism of one or more hormonesand population of databases containing the same is ongoing, GMF valuescan be periodically re-entered (e.g., upon refilling a prescription) inthe apparati, systems, computer program products, and methods providedherein. In an example embodiment, the values for W1, W2, and W3 can be0.2, 0.1. and 0.04 respectively.

With such weights, this means that the GMF value can range from 1 (ifnone of the CYP450 gene alleles are present) to 1.34 (if all of theCYP450 gene alleles are present). While certain members of the CYP450gene family are discussed in this example, it should be understood thatalleles of other CYP450 genes and non-CYP450 genes that are deemed toimpact how people metabolize or respond to steroid hormones can also betaken into consideration when computing GMF (including potentiallyalleles of any genes that are linked to decreased steroid metabolism).

It should also be understood that expression levels of theaforementioned CYP450 genes can also be used in computing GMF. Factorsincluding various genetic polymorphisms (e.g., alleles), induction byxenobiotics, regulation by cytokines, hormones and during diseasestates, as well as the sex and age of a subject can influence CYP450gene expression (Zanger and Schwab; Pharmacol Ther 2013 April;138(1):103-41. doi: 10.1016/j.pharmthera.2012.12.007).

Thus, H1DF can be computed in an example embodiment as follows:

$\begin{matrix}{\mspace{79mu} {{H\; 1{DF}} = {H\; 1{MDF} \times H\; 1{DSEF} \times {GMF}}}} & {{Equation}\mspace{14mu} (4)} \\{{H\; 1{DF}} = {\left( \frac{1}{\left\lbrack {H\; 1M} \right\rbrack} \right) \times \left( {\sum\limits_{i = 1}^{N}\; \left( {H\; 1_{S}} \right)_{i}} \right) \times \left( {1 + \left( {{W\; 1\left( {{CYP} - {1A\; 1}} \right)} + {W\; 2\left( {{CYP} - {1A\; 2}} \right)} + {W\; 3\left( {{CYP} - {1B\; 1}} \right)}} \right)} \right)}} & {{Equation}\mspace{14mu} (5)}\end{matrix}$

H2DF can be computed in a similar fashion, but where H2 measurements andH2 symptom experience data are used. The H2 Measured Deficiency Factor(H2MDF) can be computed according to an inverse relationship with themeasured level of H2 for the subject patient from assay 210. Thus, in anexample embodiment, H2MDF can be computed as:

$\begin{matrix}{{H\; 2{MDF}} = \frac{1}{\left\lbrack {H\; 2M} \right\rbrack}} & {{Equation}\mspace{14mu} (6)}\end{matrix}$

where H2M represents the measured level of H2 from assay 210. The valuefor H2M can be included as part of data 312 (see FIG. 3). Likewise, theH2 Deficiency Symptom Experience Factor (H2DSEF) can be computed as anaggregation of severity data experienced by the subject patient fordifferent symptoms of an H2 deficiency. Thus, in an example embodiment,H2DSEF can be computed as:

H2DSEF=Σ_(i=1) ^(M)(H2_(S))_(i)  Equation (7)

where M represents the total number of different symptoms associatedwith an H2 deficiency, and where (H2_(S))_(i) represents theuser-specified severity value for symptom i associated with an H2deficiency. The values for (H2_(S))_(i) can be included as part of data314 (see FIG. 3). The same GMF used for computing H1DF can be used whencomputing H2DF. Accordingly, H2DF can be computed in an exampleembodiment as follows:

$\begin{matrix}{\mspace{76mu} {{H\; 2{DF}} = {H\; 2{MDF} \times H\; 2{DSEF} \times {GMF}}}} & {{Equation}\mspace{14mu} (8)} \\{{H\; 2{DF}} = {\left( \frac{1}{\left\lbrack {H\; 2M} \right\rbrack} \right) \times \left( {\sum\limits_{i = 1}^{M}\; \left( {H\; 2_{S}} \right)_{i}} \right) \times \left( {1 + \left( {{W\; 1\left( {{CYP} - {1A\; 1}} \right)} + {W\; 2\left( {{CYP} - {1A\; 2}} \right)} + {W\; 3\left( {{CYP} - {1B\; 1}} \right)}} \right)} \right)}} & {{Equation}\mspace{14mu} (9)}\end{matrix}$

Further still, additional terms can be factored into the H1DF and H2DFcomputations if desired by a practitioner. An example of this is shownby FIG. 4B. In the example of FIG. 4C, the H1DF and H2DF computationsalso include a genetic contra-indication factor (GCIF). If the patient'sgenetic profile shows a predisposition for certain types of cancers(e.g., breast cancer, ovarian cancer, cervical cancer, uterine cancer,and/or other cancers that are metastatic or known (or believed) topossess steroid hormone-responsive cell types (e.g., hormonereceptor-positive cells, including estrogen-receptor or progesteronereceptor positive cells), this would indicate the patient is notsuitable for hormone therapy treatment. For example, loss-of-functionalleles of the BRCA1 and BRCA2 tumor suppressor genes are linked toincreased risks of breast cancer. Non-limiting examples of BRCA1 andBRCA2 loss-of-function alleles include those set forth in the NationalCenter for Biotechnology Information (NCBI) world wide web site“ncbi.nlm.nih.gov/clinvar.” Since identification of BRCA1 (NCBI Gene ID:672) or BRCA2 (NCBI Gene ID: 675) loss-of-function alleles andpopulation of databases containing the same is ongoing, GCIF values canbe periodically re-entered (e.g., upon refilling a prescription).Accordingly, the formulas for H1DF and H2DF may also include a GCIF thatoperates to zero out the H1DF and H2DF values if the patient's geneticprofile data indicates the presence of either the BRCA1 or BRCA2loss-of-function gene alleles. For example, GCIF can be computed asfollows:

GCIF=f(BRACA)  Equation (10)

where f(BRACA) will be zero the patient's genetic profile data indicatesthe presence of either the BRCA1 or BRCA2 loss-of-function gene alleles,and where f(BRACA) will be 1 otherwise. The value for BRACA can beincluded as part of data 316 (see FIG. 3). Thus, with H1DF and H2DFbeing computed as shown below, it can be seen that H1DF and H2DF willzero out if the patient's genetic profile shows the presence of eitherthe BRCA1 or BRCA2 loss-of-function gene alleles:

H1DF=H1MDF×H1DSEF×GMF×GCIF  Equation (11)

H2DF=H2MDF×H2DSEF×GMF×GCIF  Equation (12)

However, it should be understood that such treatment contra-indicatorsneed not be factored directly into the H1DF and H2DF computations ifdesired by the practitioner. For example, the FIG. 4 process flow couldinclude conditions at the outset where no H1DF or H2DF values will becomputed if any contra-indications are present.

In another example embodiment, the analysis program 304 can also takeinto consideration additional medical data about the subject patientwhen recommending hormone therapy treatment prescriptions. For example,as shown by FIG. 5, the memory 302 can also store medical data 500 aboutthe patient, and this data 500 can be used by analysis program 502 whendeciding on an appropriate prescription recommendation. As an example,the medical data 500 can be data indicative of the patient's body fatcomposition (e.g., a BMI or BFP for the patient). Steroid hormones arefat soluble and therefore are deposited within and attain higherconcentrations in fatty tissues. Thus, the volume of distribution ofhormones is directly proportional to the amount of fat present in thepatient. Patients who have higher amounts of fatty tissues are expectedto require higher dosages of administered steroid hormones to attain thesame active (blood/serum) levels as a person with less fatty tissue. BMIcan serve as a measure of a patient's lean vs. fat mass, and can thus beused to establish the relative volume of distribution of fat-solubledrugs, including all steroid hormones. However, it should be understoodthat medical characteristics other than BMI could be used, such as BFP.In certain embodiments, BFP data obtainable by skinfold caliper,Dual-Energy X-ray Absorptiometry (DXA), hydrostatic weighing, airdisplacement plethysmography, bioelectric impedance analysis (BIA),bioimpedance spectroscopy (BIS), Electrical Impedance Myography (EIM),3-D body scans, and multi-compartment models using certain combinationsof the aforementioned techniques can be used. Also, the system may useother types of medical information about the patient as medical data500. For example, a patient's medication history can be used as part ofdata 500 to influence the analysis program 502. As an example, if thepatient is currently taking birth control medication, the system may usethis data as a contra-indicator that causes no hormone therapy involvingprogesterone or estrogen to be recommended for the patient.

Similarly, if the medication history shows the patient being treatedwith other sex hormones as part of a menopause treatment for thepatient, the system may use this data as a contra-indicator that causesno hormone therapy involving progesterone or estrogen to be recommendedfor the patient. As yet another example, a patient's surgical historycan be used to influence how the computer system selects a recommendedprescription. As an example, surgical or medical histories showing theexistence of and/or treatments for certain types of cancers (e.g.,breast cancer, ovarian cancer, cervical cancer, uterine cancer, and/orother cancers that are metastatic and/or known (or believed) to possesssteroid-responsive cell types) may be used by the system as acontra-indicator that causes no hormone therapy involving progesteroneor estrogen to be recommended for the patient.

Further still, regarding the medical or genetic information used fordata 316 and 500, it should be understood that a practitioner may chooseto implement the system 200 so that such data need not necessarily bethe patient's direct data. A practitioner may, in some situations, deemit appropriate to use genetic or medical information from a relative ofthe patient (such as a first degree relative) to serve as thepatient-specific data. For example, if a patient has a history offamilial breast cancer, which may take the form of a 1^(st) degreerelative at a young age, this data may be used to represent thepatient's medical information.

FIG. 6A depicts an example process flow for analysis program 502 in FIG.5. At step 600, the processor computes H1DF for Patient X based onPatient X's H1 measurement (see data 310), H1 deficiency symptom data(see data 314), genetic profile data (see data 316), and medical data(see data 500). At step 602, the processor computes H2DF for Patient Xbased on Patient X's H2 measurement (see data 312), H2 deficiencysymptom data (see data 314), genetic profile data (see data 316), andmedical data (see data 500). Examples of techniques that can be used forcomputing H1DF and H2DF are discussed below. Once the H1DF and H2DFvalues are computed at steps 600 and 602, the processor can access thedata structure 410 to look up the appropriate recommended prescriptionas discussed above with reference to step 404.

FIG. 6B shows examples of functions that can be used by program 502 tocompute H1DF and H2DF. H1DF can be computed as a combination of factors,including H1MDF, H1DSEF, and GMF as discussed above and further incombination with a patient fatty tissue dosage factor (FTDF). Similarly,H2DF can be computed as a combination of factors, including H2MDF,H2DSEF, and GMF as discussed above and further in combination with theFTDF.

In an example embodiment, FTDF can be computed as follows:

$\begin{matrix}{{FTDF} = \frac{BMI}{30}} & {{Equation}\mspace{14mu} (13)}\end{matrix}$

where BMI is computed according to the formula:

$\begin{matrix}{{BMI} = \frac{{weight}({kg})}{{{height}(m)}^{2}}} & {{Equation}\mspace{14mu} (14)}\end{matrix}$

Since height is commonly measured in centimeters, an alternate BMIcalculation formula, dividing the weight in kilograms by the height incentimeters squared, and then multiplying the result by 10,000, can beused. When using English measurements, BMI can be calculated by dividingweight in pounds (lbs) by height in inches (in) squared and multiplyingby a conversion factor of 703. The value for BMI can be included as partof data 500 (see FIG. 5).

Clinically, patients with a BMI over 30 generally require a higheradministered hormone dose to achieve the required blood, serum, and/ortissue levels. Thus, a patient with a BMI of 35 requires a higher doseof a steroid hormone than would another patient who has a BMI of 20.Research by the inventor has established a clinical dosage baseline fora BMI of 30, above and below which the patient will require more or lesshormone. Accordingly, the FTDF formula will yield the following resultsfor the following different patients:

-   -   A 140 lb person with a height of 5′10″ with a BMI of 20 would        have an FTDF of 20/30, which equals 0.666.    -   A 170 lb person with a height of 5′10″ with a BMI of 25 would        have an FTDF of 25/30, which equals 0.834.    -   A 210 lb person with a height of 5′10″ with a BMI of 30 would        have an FTDF of 30/30, which equals 1.0.    -   A 245 lb person with a height of 5′10″ with a BMI of 35 would        have an FTDF of 35/30, which equals 1.16.    -   A 280 lb person with a height of 5′10″ with a BMI of 40 would        have an FTDF of 40/30, which equals 1.33.        Thus, a 280 pound, 5′10″ patient with a BMI of 40 will have a        suggested hormone dose roughly 1.3 times the amount of a person        with a BMI of 30, and roughly 1.5 times the amount of a much        thinner person with a BMI of 25. Also, as noted above, it should        be understood that measurements of a patient's body fat        composition characteristic other than BMI could be used to        compute an FTDF value if desired by a practitioner.

Thus, FIG. 6B shows computations of H1DF and H2DF as follows:

H1DF=H1MDF×H1DSEF×GMF×FTDF  Equation (15)

H2DF=H2MDF×H2DSEF×GMF×FTDF  Equation (16)

FIG. 6C shows an alternate approach to computing H1DF and H2DF for useby analysis program 502, where the H1DF and H2DF calculations also takeinto consideration FTDF and GCIF as discussed above. Thus, FIG. 6C showscomputations of H1DF and H2DF as follows:

H1DF=_(H)1MDF×_(H)1DSEF×GMF×GCIF×FTDF  Equation (17)

H2DF=_(H)2MDF×_(H)2DSEF×GMF×GCIF×FTDF  Equation (18)

Based on the computed values for H1DF and H2DF, an appropriaterecommended prescription can then be located and selected from datastructure 410.

Accordingly, methods are thus described for providing hormonereplacement therapy to a person comprising use of any of theaforementioned apparati, systems, and computer programs. In certainembodiments, such methods can comprise: (a) determining or obtaining theperson's levels of a first and a second hormone; (b) determining orobtaining from the person an allelic status and/or an expression levelof: (i) at least one gene which affects metabolism of the first and/orsecond hormones; and (ii) a gene linked to an adverse health risk for apatient if the person is treated with the first and/or second hormones;and (c) administering to the person an effective dose of a formulationcomprising: (i) the first hormone, an analog thereof, a precursorthereof, and/or a metabolite thereof; and (ii) the second hormone, ananalog thereof, a precursor thereof, and/or a metabolite thereof,wherein said formulation and dose are obtained by using any apparatus,computer program, and/or system provided herein. In certain embodiments,such methods can comprise administering to the person an effective doseof a formulation comprising: (i) the first hormone, an analog thereof, aprecursor thereof, and/or a metabolite thereof; and (ii) the secondhormone, an analog thereof, a precursor thereof, and/or a metabolitethereof, wherein said formulation and dose are obtained by using anyapparatus, computer program, and/or system provided herein. In certainembodiments, such methods can comprise: (a) determining the person'slevels of a first and a second hormone by obtaining a biological samplefrom the person and measuring the levels of the first and secondhormone; (b) performing a genotyping assay to determine allelic statusof: (i) at least one gene which affects metabolism of the first and/orsecond hormones; and (ii) a gene linked to an adverse health risk for apatient if the person is treated with the first and/or second hormones;and (c) administering to the person an effective dose of a formulationcomprising: (i) the first hormone, an analog thereof, a precursorthereof, and/or a metabolite thereof; and (ii) the second hormone, ananalog thereof, a precursor thereof, and/or a metabolite thereof,wherein said formulation and dose are obtained by using any apparatus,computer program, and/or system provided herein. In certain embodiments,the methods comprise determining or obtaining a deficiency factor valuefor a first hormone based on an analysis of (1) a measured level of thefirst hormone in the person, (2) symptom experience data for the personwith respect to a plurality of symptoms that relate to a plurality ofconditions associated with a deficiency of the first hormone in a human,and (3) genetic profile data for the person; determining or obtaining adeficiency factor value for a second hormone based on an analysis of (1)a measured level of the second hormone in the person, (2) symptomexperience data for the person with respect to a plurality of symptomsthat relate to a plurality of conditions associated with a deficiency ofthe second hormone in a human, and/or (3) genetic profile data for theperson; selecting or obtaining an effective formulation and dose of thefirst and second hormones, analogs thereof, precursors thereof, and/or ametabolites thereof for treating the person based on the determinedfirst and second hormone deficiency factor values; and administering tothe person the selected effective formulation and dose. In any of theaforementioned contexts, terms such as “determining,” “obtaining,”“performing,” “administering,” “assaying,” “genotyping,” “assaying,” andthe like can refer to actual execution of the act or to causing the actto be executed by another. Administering can be effected by any routeappropriate for the effective delivery of the formulation (e.g., topicaland/or parenteral routes that include cutaneous, sub-cutaneous,intravenous, intramuscular, oral, rectal, vaginal, transdermal, and/orsublingual delivery) In certain embodiments, determination of a firstand an second hormone level can be performed by assay provider which canreceive and analyze a biological sample from parties that include apatient or a healthcare practitioner who obtains the sample from thepatient. In certain embodiments, genotyping can be performed by agenotypic assay provider which can receive and genotype a biologicalsample from a patient or a healthcare practitioner who obtains thesample from the patient. Similarly, administration of the formulationcan be performed by a patient or a healthcare practitioner. In certainembodiments of any of the aforementioned methods, the levels of thefirst and/or second hormones, the allelic status of one or more of thegenes, and/or the expression levels of one or more of the genes can beobtained from a database associated with any apparatus, computerprogram, and/or system provided herein. In certain embodiments of any ofthe aforementioned methods, the first hormone is progesterone and thesecond hormone is estrogen. In certain embodiments of any of theaforementioned methods, the formulation further comprises a thirdhormone, an analog thereof, a precursor thereof, and/or a metabolitethereof. wherein the third hormone comprises testosterone. In certainembodiments, any of the aforementioned methods can further comprisedetermining or obtaining a body fat composition characteristic for useby the apparatus, computer program, or system. In certain embodiments,the body fat composition characteristic can be obtained from a databaseassociated with the apparatus, computer program, or system. In certainembodiments of any of the aforementioned methods, the gene which affectsmetabolism of the first and/or second hormones is a member of theCytochrome P450 (CYP) gene family. In certain embodiments, the member ofthe Cytochrome P450 (CYP) gene family member is CYP1A1, CYP1A2, and/orCYP1B1. the gene linked to an adverse health risk is a BRCA1 gene and/orBRCA2 gene. Expression level of genes can be measured by any effectivehybridization-, amplification-, mass spectrometry, and/or nanopore basedtechnique. Non-limiting examples of such techniques for measuring geneexpression levels are disclosed in U.S. Pat. Nos. 9,624,534, 9,617,584,9,964,538, and 10,048,245, which are each incorporated herein byreference in their entireties. In certain embodiments of any of theaforementioned methods, the second hormone is estrogen, and theformulation comprises: (i) progesterone, an analog thereof, a precursorthereof, and/or a metabolite thereof, and (ii) about 80% estradiol andabout 20% estriol by weight. In certain embodiments, a high doseprogesterone/low dose estrogen therapy is indicated and the formulationcomprises: (i) progesterone at a concentration of about 100 mg/mL; and(ii) a composition of about 80% estradiol and about 20% estriol byweight at a concentration of about 0.4 mg/mL. In certain embodiments, anequal clinical strength progesterone/estrogen therapy is indicated andthe formulation comprises: (i) progesterone at a concentration of about80 mg/mL; and (ii) a composition of about 80% estradiol and about 20%estriol by weight at a concentration of about 2 mg/mL. In certainembodiments, a low dose progesterone/high dose estrogen therapy isindicated and the formulation comprises: (i) progesterone at aconcentration of about 40 mg/mL; and (ii) a composition of about 80%estradiol and about 20% estriol by weight at a concentration of about 4mg/mL. In certain embodiments of any of the aforementioned methods, thefirst hormone is progesterone, the second hormone is estradiol, and thethird hormone is testosterone, and wherein the formulation comprises:(i) progesterone, an analog thereof, a precursor thereof, and/or ametabolite thereof; (ii) about 80% estradiol and about 20% estriol byweight; and (iii) dehydroepiandrosterone (DHEA). In certain embodiments,a high dose progesterone/low dose estrogen/low dose testosterone therapyis indicated and the formulation comprises: (i) progesterone at aconcentration of about 100 mg/mL; (ii) a composition of about 80%estradiol and about 20% estriol by weight at a concentration of about0.4 mg/mL; and (ii) DHEA at a concentration of about 6 mg/mL. In certainembodiments, an equivalent dose progesterone/estrogen and a low dosetestosterone therapy is indicated and the formulation comprises: (i)progesterone at a concentration of about 80 mg/mL; (ii) a composition ofabout 80% estradiol and about 20% estriol by weight at a concentrationof about 2 mg/mL; and (ii) DHEA at a concentration of about 6 mg/mL. Incertain embodiments, a low dose progesterone/high dose estrogen/low dosetestosterone therapy is indicated and the formulation comprises: (i)progesterone at a concentration of about 40 mg/mL; (ii) a composition ofabout 80% estradiol and about 20% estriol by weight at a concentrationof about 4 mg/mL; and (ii) DHEA at a concentration of about 6 mg/mL. Incertain embodiments, a high dose progesterone/low dose estrogen/highdose testosterone therapy is indicated and the formulation comprises:(i) progesterone at a concentration of about 100 mg/mL; (ii) acomposition of about 80% estradiol and about 20% estriol by weight at aconcentration of about 0.4 mg/mL; and (ii) DHEA at a concentration ofabout 6 mg/mL. In certain embodiments, an equal clinical strengthprogesterone/estrogen and high dose testosterone therapy is indicatedand the formulation comprises: (i) progesterone at a concentration ofabout 80 mg/mL; (ii) a composition of about 80% estradiol and about 20%estriol by weight at a concentration of about 2 mg/mL; and (ii) DHEA ata concentration of about 6 mg/mL. In certain embodiments, a low doseprogesterone/high dose estrogen/high dose testosterone therapy isindicated and the formulation comprises: (i) progesterone at aconcentration of about 40 mg/mL; (ii) a composition of about 80%estradiol and about 20% estriol by weight at a concentration of about 4mg/mL; and (ii) DHEA at a concentration of about 6 mg/mL.

In certain embodiments of the aforementioned apparati, systems, computerprograms and methods, the hormones of interest are thyroid hormones(e.g., thyroxine (T4) and/or triiodothyronine (T3)) and/orThyroid-Stimulating Hormone (TSH) and the patients have thyroidhormone-associated disorders (e.g., hyperthyroidism or hypothyroidism).Symptoms associated with hypothyroidism that are tracked includefatigue, cold, constipation, weight gain, hoarseness, muscle weakness,elevated blood cholesterol, muscle aches, pain, heavier or irregularmenstrual periods, thinning hair, slowed heart rate, depression, andimpaired memory. Such hypothyroidism systems are addressed by thyroidhormone therapy (administration of synthetic T3, T4, analogues thereof,precursors thereof, or any combination thereof). Symptoms associatedwith hyperthyroidism that are tracked include sudden weight loss (evenwithout a decrease in appetite and food intake), rapid heart ratefatigue, increased appetite, nervousness, anxiety, irritability,tremors, changes in menstrual periods, increase heat sensitivity,changes in bowel patterns, enlarged thyroid, fatigue, muscle weakness,difficulty sleeping, and fine or brittle hair. Such hypothyroidismsystems are addressed by thyroid hormone suppression therapy (e.g.,administration of propylthiouracil, methimazole, and/or carbimazole).

Example Use Case: Progesterone and Estrogen

As noted above, in an example embodiment, the patient can be a woman whomay be experiencing a deficiency of progesterone and/or estrogen (e.g.,a patient who may be experiencing menopause symptoms). With such anexample use case, H1 can be progesterone and H2 can be estrogen, inwhich case H1DF serves as a progesterone deficiency factor (PDF) andH2DF serves as an estrogen deficiency factor (EDF). It is expected thatsuccessful treatment of a woman's menopause symptoms arising fromhormone deficiencies will need the proper amount of estrogen andprogesterone (the dose) and their relative ratio within theprescription.

With such an example, the data structure 410 used by the computer system202 can be arranged as an array or grid of cells 702 as shown by FIGS.7A-D. Each cell 702 in the array is indexed by values of EDF on they-axis and PDF on the x-axis and can be populated with data thatidentifies the recommended prescription of progesterone and estrogen forthose associated values of EDF and PDF. Accordingly, if the computed PDFand EDF values for a given patient fall in ranges that map to aparticular cell 702, the processor can select the prescription linked tothat cell 702 as the prescription to be recommended for the patient.

FIG. 7A shows how EDF values can be arranged on the y-axis 700 of thedata structure 410 so that the minimum EDF value is at the bottom andthe maximum EDF value is at the top. This means that as one moves upwardin the data structure 410, this will translate into increasing estrogenrequirements 704 for the prescription. FIG. 7B shows how PDF values canbe arranged on the x-axis 710 of the data structure 410 so that theminimum PDF value is at the left and the maximum PDF value is at theright. This means that as one moves rightward in the data structure 410,this will translate into increasing progesterone requirements 714 forthe prescription. FIG. 7C shows an overlay of FIGS. 7A and 7B, whichreveals a relationship where movement in the data structure from thebottom left toward the top right translates into increasing dosage 724for the prescription (regardless of relative mix of estrogen andprogesterone in the prescription).

Furthermore, FIG. 7D illustrates the combination of the PDF and EDF in agrid of cells 702, each representing the requirement of estrogen andprogesterone (in relative ratios) in a prescription which willadequately treat a patient with known symptoms of estrogen andprogesterone deficiency and whose blood (or other body fluid)concentration (deficiency) of estrogen and progesterone is known. Withinthis grid, it can be seen that there is an area where estrogenrequirements in the final prescription are higher than progesterone (thezone in the upper left where estrogen deficiency symptoms are dominant),an area where estrogen and progesterone requirements in the finalprescription are similar (the middle zone where the progesteronedeficiency symptoms and the estrogen deficiency symptoms are roughlyequivalent), and an area where progesterone requirements in the finalprescription are higher than estrogen (the zone in the lower right whereprogesterone deficiency symptoms are dominant).

This translates to (1) the upper left zone having a higher clinicaldosage of estrogen relative to progesterone, (2) the middle zone havingroughly equal clinical dosages of estrogen and progesterone, and (3) thelower right zone having a higher clinical dosage of progesteronerelative to estrogen.

Although not illustrated by FIG. 7D, the dose of the prescriptionincreases from bottom left to upper right, as it did in FIG. 7C. Notethat the amount of each hormone required to treat a patient willincrease diagonally from bottom left to upper right. Those at the upperright have more symptoms and lower body fluid levels of both of thedeficient hormones than those at the bottom left of the grid (wherepeople with no symptoms and normal blood levels reside). Those at theupper right require the same relative concentrations of estrogen andprogesterone in their prescription, but they require more of it (e.g., ahigher dose per day) than those in the middle of the grid or towards thelower left corner of the grid.

The data structure 410 in the progesterone/estrogen example can also bescaled to have its values along the x-axis and y-axis match up withranges of values expected for the PDF and EDF values. Toward this end,one can consider the ranges of values expected for the constituentcomponents of PDF and EDF.

Measured Progesterone Level and Progesterone Deficiency Symptoms

In applying Equation (1) above, the goal is to compute a baseline ofprogesterone desired in a prescription for a peri menopausal ormenopausal woman that allows for the desired clinical outcome: relief ofsymptoms, therapeutic levels in body fluids, without overdosing.Progesterone levels change normally during a woman's monthly cycle. Thismonthly variation abates once menopause has occurred and blood levelsdrop to largely constant low levels. When treating a patient, a lowerprogesterone concentration in the patient's body fluids will indicate ahigher progesterone concentration in the prescribed medication. By usingthe inverse of the absolute concentration, Equation (1) dictates thatthe smaller the concentration of progesterone measured in the fluid willyield a higher concentration of progesterone in the final prescription.This calculated number reflects the actual deficiency of this hormone inthe patient's blood. The normal range for progesterone in the blood ofpost-menopausal women is 0.1-0.6 ng/ml, and in the luteal phase ofpre-menopausal women the range is 0.2-0.8 ng/ml. We can see levels aslow as 0.05 ng/ml in symptomatic postmenopausal women. Thus, values forH1DF (where H1 is progesterone) can be real numbers ranging from1/0.05=20 at one end to 1/0.8=1.25 at the other end (a range of1.25->20). It should be understood that these values are correlated tomeasured progesterone levels in blood samples, and these numbers willchange based upon the normal range in different body fluids if samplesother than blood are used for assay 210; but the final data structure410 need not be affected by such variations. Furthermore, it should benoted that an astute physician would typically not want to prescribeprogesterone to a woman whose serum progesterone is in the normal range,thus the formula for Equation (1) can be further designed to translateany value lower than 1.25 as a zero value.

In applying Equation (2) above, there are 6 symptoms commonly associatedwith progesterone deficiency in women (hence N=6 in Equation (2)). Thesesymptoms are (1) anxiety, (2) crankiness, (3) painful and/or lumpybreasts, (4) unexplained weight gain, (5) insomnia, and (6) cyclicalheadaches. Since women experience these symptoms in a very individualway, the system allows the patient to specify severity values for thesesymptoms based on their own perceptions and experience. As noted aboveand below, a mobile application and/or web application can be providedto obtain such symptom experience data from patients.

For example, the patient can be prompted for a percentage value toindicate the severity of each symptom, in which case 0% corresponds tothe minimum severity and 100% corresponds to the maximum severity. Theseseverity percentages can be translated into numbers, e.g., 0% can berecorded as 0.167 and 100% can be recorded as 1.0, and percentagesbetween 0% and 100% can be scaled linearly between 0.167 and 1.0 ascalculated to the 1/10^(th) (three significant digits). Accordingly, itcan be seen that with such an example arrangement, the well-controlledpatient with no symptoms of progesterone deficiency would have an H1DSEFof 1 (based on 6 symptoms with a symptom experience score for each ofthe minimum 0.167). By contrast, a patient with maximally severesymptoms of progesterone deficiency would have an H1DSEF of 6 (based on6 symptoms with a symptom experience score for each of the maximum 1.0).

Given that Equations (4), (11), (15), and (17) each multiply H1DF byH1DSEF, it can be seen that the range for PDF needs to encompass atleast 1×1.25 to 6×20; or 1.25->120, which can include a zero factor toaccount for patient's whose measurement levels do not indicatetreatment, and thus range from 0 to 120. Furthermore, in an exampleembodiment as noted above where the GMF values may range from 1->1.34,this means that the upper end of the range for H1DF according toEquation (4) may reach to roughly 160. Further extensions of the uppervalue for the H1DF range may be desirable to accommodate the influenceof FTDF under Equation (15).

Measured Estrogen Level and Estrogen Deficiency Symptoms

In applying Equation (6) above, the goal is to compute a baseline ofestrogen desired in a prescription for a peri menopausal or menopausalwoman that allows for the desired clinical outcome: relief of symptoms,therapeutic levels in body fluids, without overdosing. By using theinverse of the absolute concentration, Equation (1) dictates that thesmaller the concentration of estrogen measured in the fluid will yield ahigher concentration of estrogen in the final prescription. Thiscalculated number reflects the actual deficiency of this hormone in thepatient's blood. The normal blood levels for estrogen (estradiol) (E2)for menstruating women range from 30 to 350 pg/ml. For postmenopausalwomen, normal levels are typically below 30 pg/ml, and the level atwhich it a practitioner may find it undesirable to provide estrogen to apatient would be 30 pg/ml, and levels are not typically found below 0.1pg/ml. Thus, values for H2DF (where H2 is estrogen) can be real numbersranging from 1/30=0.033 (which can be rounded to 0.04) at one end to1/0.1=10 at the other end (a range of 0.04->10). It should be understoodthat these values are correlated to measured estrogen levels in bloodsamples, and these numbers will change based upon the normal range indifferent body fluids if samples other than blood are used for assay210; but the final data structure 410 need not be affected by suchvariations. Furthermore, it should be noted that the formula forEquation (6) can be further designed to translate any value lower than0.04 as a zero value.

In applying Equation (7) above, there are 5 symptoms commonly associatedwith estrogen deficiency in women (hence M=5 in Equation (7)). Thesesymptoms are (1) vaginal dryness, (2) painful intercourse, (3) hotflashes, (4) night sweats, and (5) lethargy/depression. Since womenexperience these symptoms in a very individual way, the system allowsthe patient to specify severity values for these symptoms based on theirown perceptions and experience. As noted above and below, a mobileapplication and/or web application can be provided to obtain suchsymptom experience data from patients. For example, the patient can beprompted for a percentage value to indicate the severity of eachsymptom, in which case 0% corresponds to the minimum severity and 100%corresponds to the maximum severity. These severity percentages can betranslated into numbers, e.g., 0% can be recorded as 0.2 and 100% can berecorded as 1.0, and percentages between 0% and 100% can be scaledlinearly between 0.2 and 1.0 as calculated to the 1/10^(th) (threesignificant digits). Accordingly, it can be seen that with such anexample arrangement, the well-controlled patient with no symptoms ofestrogen deficiency would have an H2DSEF of 1 (based on 5 symptoms witha symptom experience score for each of the minimum 0.2). By contrast, apatient with maximally severe symptoms of estrogen deficiency would havean H2DSEF of 5 (based on 5 symptoms with a symptom experience score foreach of the maximum 1.0).

Given that Equations (8), (12), (16), and (18) each multiply H2DF byH2DSEF, it can be seen that the range for EDF needs to encompass asleast 1×0.04 to 5×10; or 0.04->50, which can include a zero factor toaccount for patient's whose measurement levels do not indicatetreatment, and thus range from 0 to 50. Furthermore, in an exampleembodiment as noted above where the GMF values may range from 1->1.34,this means that the upper end of the range for H2DF according toEquation (8) may reach to roughly 67. Further extensions of the uppervalue for the H2DF range may be desirable to accommodate the influenceof FTDF under Equation (16).

Accordingly, in the data structure 410 of FIG. 7D, for an exampleembodiment, the range on the y-axis can go from 0 at the bottom to 50 atthe top, and the range on the x-axis can go from 0 at the far left to120 at the far right. Furthermore, a practitioner can divide this 50×120grid space into a number of cells 702 where each cell corresponds tosome range of values with respect to EDF and PDF. With this example, ifthe values for GMF and/or FTDF cause the computed H1DF and/or H2DFvalues to exceed the grid range, the system can operate to select themaximal H1DF and/or H2DF value as applicable. However, in other exampleembodiments, the data structure 410 may include y-axis and x-axis rangesthat go beyond 50 and 120 respectively so that the upward adjustmentscaused by GMF and/or FTDF can be directly encoded in the cells 702 ofthe grid.

Formula and Dose of Estrogen and Progesterone within a Prescription GridData Structure

Continuing with the example of FIG. 7D, the grid data structure for PDFand EDF can have 50 rows (0 to 50) and 60 columns (0 to 120, by two's),which correspond to the EDF and PDF as described above. Each cell 702can be populated by a three digit code (E,P,D) corresponding to itsestrogen number (Y axis), progesterone number (x axis) and dose beingestablished from bottom left to upper right. The x (P) and y (E)coordinates of this grid will dictate the final concentration ofestrogen and progesterone (respectively) in the final prescription (theformula of the drugs, in mg/ml), with the D component corresponding tothe dose prescribed (how much of the formula, and how often) which wasestablished previously as increasing from lower left to upper right (andwill be modified subsequently).

The PIE Ratio and the Recognition of “Estrogen Dominance” MenopauseSymptoms

The ratio of measured (blood/serum/saliva/urine) progesterone toestrogen is helpful in clinical practice when providing an appropriatehormone replacement therapy prescription. These situations give rise toclinical syndromes known as “Estrogen Symptom Dominance” and“Progesterone Symptom Dominance” and are characterized by symptomstypical of deficiencies of one or the other hormone but it is morecomplex than that with their relative ratios being of clinicalsignificance and not necessarily the true levels. Thus, the P/E Ratiocan be a component of the treatment system 200. By the very nature ofthe table comparing the EDF (incorporating both symptoms of estrogendeficiency and measured hormone levels) and the PDF (incorporatingsymptoms and hormone levels), the entire relationship (the grid)represents a ratio of progesterone to estrogen symptoms and ultimatelyrequirements within the ultimate prescription. As shown by FIG. 7D, theareas of estrogen symptom dominance and progesterone symptom dominanceare readily contained and represented within this methodology.

High PIE Ratio

A high P/E ratio occurs when estrogen is low relative to progesterone.This describes the classic situation of Estrogen Symptom Dominance andillustrated in the top left corner of FIG. 7D as noted above. Here thesymptoms of estrogen deficiency (and EDF) are high and the symptoms ofprogesterone deficiency (and PDF) are low. Within the methodology here,increasing estrogen (with or without decreasing progesterone) in theprescribed hormone therapy is appropriate.

Low PIE Ratio

A low P/E ratio occurs when measured serum (blood, saliva) progesteroneis low relative to estrogen. This is progesterone symptom dominance andis represented in the bottom right of FIG. 7D as noted above. Thisaspect is incorporated into our methodology and is associated withhigher doses of progesterone in the final prescription.

Theoretically, the estrogen and progesterone example discussed hereinprovides for at least 6,000 different and unique prescription formulasfor estrogen and progesterone (50×120). This may be appropriate wherethe prescriptions are filled by an automated machine which can mix thedrug as prescribed; however it becomes cumbersome if the prescriptionsare filled by hand. Moreover, supporting such a huge number of possiblevariations in formulations and dosage also makes high volume batchproduction impractical. However, the ability to mass produceformulations of progesterone/estrogen treatments would be highlybeneficial in that it could help decrease costs to patients and provideother benefits such as increased sterility and dose-to-dose consistencyin potency and makeup.

Further still, the data structure 410 described herein comprised of anarray of cells 702 indexed by H1DF and H2DF values allows for the datastructure 410 to be readily scaled up or down to more or fewer number ofavailable prescription formulas. For example, a table with 6,000 uniquecells 702 could be readily reduced to 100 unique cells 702 by combiningevery 5 rows of the table into a single row and by combining every 12columns of the table into a single column, thereby yielding a 10×10table. Managing 100 unique formulations/dosages of H1 and H2 may be moremanageable for practitioners. Moreover, because of the spatial andlinear relationships along the two axes, the scale can be readilyreduced (or increased) based on the level of granularity inrecommendations that a practitioner desires for the system. That is,data structure 410 as shown by FIG. 7D allows for the grouping of womenwith similar symptoms and hormone levels as necessary to bring the finalnumber of choices for the ultimate hormone prescription down to areasonable number with the goal that they may be treated to the desiredclinical endpoints (symptomatic relief and bone protection) at thelowest possible dose, achieved via the fewest possible formulations.

Formulations of Estrogen and Progesterone:

Based on clinical testing and the review of approximately 10,000prescriptions written by over 1000 physicians for bioidentical hormonereplacement therapy, the inventor has concluded that only 3 relativeconcentrations of estrogen and progesterone are needed to successfullytreat >95% of menopausal (and perimenopausal) women to the desiredclinical endpoint of (1) lowest possible dose to achieve (2) symptomaticrelief, and (3) long-term bone protection. As established by the datastructure 410 of FIG. 7D, the three formulations of estrogen andprogesterone will have (1) one where estrogen is provided in higherclinical concentrations relative to progesterone, (2) one whereprogesterone is provided in higher clinical concentrations relative toestrogen, and (3) one where the two are provided in relative equalclinical strengths. FIG. 7E shows a table that identifies examples ofthese 3 formulations. It should be understood that FIG. 7E is merely anexample; some practitioners may choose to employ a greater number offormulations, and or formulations with different mixes of clinicalstrengths for estrogen and progesterone.

In FIG. 7E, Formula 1 is progesterone-predominant (progesterone isprovided in a higher clinical concentration relative to estrogen).Formula 2 provides doses of estrogen and progesterone in equal clinicalstrengths. Formula 3 is estrogen-predominant (estrogen is provided in ahigher clinical concentration than progesterone).

FIG. 7F shows the formulations of FIG. 7E being mapped into cells 702 ofdata structure 410. The prescriptions in each cell 702 are identified bya pair of numbers indicating Formula Number and Dosage; hence a value of(3,2) translates to Formula 3 with a dosage of 2 units. The units ofdosage can vary depending on the modes of administration chosen by apractitioner. For example, hormone treatments are often administered astopical creams that are applied to a skin surface, where the creamembodies the prescribes formulation of progesterone and estrogen.

Dispensers such as the dispenser 1100 shown by FIG. 11 can be used todispense such cream to the patient in the proper amount for each dose.FIG. 11 shows a Topi-CLICK drug delivery system that can be suitable foruse to dispense prescribed hormone therapy treatments in exampleembodiments. The Topi-CLICK system is a cream dispensing systemavailable from DoseLogix, Inc. of Woodstock, Ga.

For example, the dispenser 1100 can be arranged to dispense a definedamount of cream in response to a patient action on the dispenser (e.g.,often referred to as “clicks”). Thus, each click can cause the dispenserto dispense an amount of cream such as in 0.5 ml aliquots. In such asituation, dosage units of 1 may correspond to 1 click of cream per day,dosage units of 2 may correspond to 2 clicks of cream per day, dosageunits of 3 may correspond to 3 clicks of cream per day, and dosage unitsof 4 may correspond to 4 clicks of cream per day. However, these aremerely examples and other dose units can be employed by a practitionerif desired.

Forms of Estrogen Used in Clinical Medicine

As noted above, estrogen may be prescribed in any of a number of forms.For example, Biest is a common form of estrogen used with hormonetherapy treatment. Biest is a combination of estradiol (E2) and estriol(E3). Estradiol is a fairly strong form of estrogen with strongactivation of estrogen receptors on target cells. Estriol has a longerhalf-life and activates target receptors to a much lesser degree; thusit “buffers” and balances the stronger, more “aggressive” estradiol.

Estradiol treatment by itself can cause breast tenderness, furtherexacerbate fibrocystic breast disease, weight gain in the stomach,increase risk for uterine cancers by increasing the endometrialproliferation/lining and moodiness. Estriol treatment alone is too weakfor most women's menopausal symptoms. Which is why by treating withBiest, the estriol can reduce the powerful effects of estradiol butstill achieve successful menopausal relief and anti-aging. The mostcommon form of Biest used world-wide is an 80:20 ratio; with estriolencompassing 80% of the dose and estradiol 20%. Example embodimentsdescribed herein will employ Biest as the active form of estrogen in theprescription formulations. However, it should be understood that theseare merely examples; and other practitioners may find it more desirableto use other forms of estrogen in the prescription formulations.

User Interface Applications

As noted above, the system 200 can include applications such as a mobileapplication and/or web application that interface with various users ofthe system. For example, FIG. 8A depicts an example system where amobile application can be used to collect symptom experience data 314from patients. A mobile application can be installed and executed from amobile computing device 800 such as a smart phone or tablet computer.The mobile computing device 800 can communicate with the AI computersystem 202 via a network 802. The network 802 can be any suitablenetwork or networks for communicating data between the mobile computingdevice 800 and computer system 202 (e.g., a wireless network, a cellulardata network, and/or the Internet). The mobile application executed bymobile computing device 800 can cause one or more user interfaces to bepresented on a display screen of the mobile computing device 800.Through such user interface(s), the patient can provide his or hersymptom experience data 314, and the mobile computing device 800 cancommunicate such symptom experience data 314 to the computer system 202via network 802.

FIG. 8B depicts an example mobile application 810 for use with FIG. 8A.The mobile application 810 can include a plurality of graphical userinterface (GUI) screens 812 through which the patient can interact withthe mobile application 810. A control program 814 can then interact withthe GUI screens 812 and various native features of the mobile computingdevice via I/O programs 816. For example, the I/O programs 810 caninclude a GUI data out interface 818 which interfaces the GUI screens812 with display rendering capabilities of the mobile computing device800 so that the GUI screens 812 can be presented to the patient via thedisplay screen of the mobile computing device 800. The I/O programs 810can also include a GUI data in interface 820 which interfaces the GUIscreens 812 with the data input (e.g., touchscreen input) capabilitiesof the mobile computing device 800 so that the mobile application 810can collect any input from the patient via the GUI screens 812. The I/Oprograms 810 can also include a wireless data out interface 822 whichinterfaces the mobile application 810 with the wireless data (e.g., WiFior cellular data) communication capabilities of the mobile computingdevice 800 so that the mobile application 810 can send the symptomexperience data 314 to the computer system 202. Further still, the I/Oprograms 810 can also include a wireless data in interface 824 whichinterfaces the mobile application 810 with the wireless data (e.g., WiFior cellular data) communication capabilities of the mobile computingdevice 800 so that the mobile application 810 can receive wireless datacommunications from remote computer systems such as computer system 202.

FIGS. 8C and 8D depict example user interfaces for the mobileapplication 810. FIG. 8C shows an example GUI 850 that is designed tosolicit and receive inputs from the patient that quantify hormonedeficiency symptoms (e.g., symptoms of a progesterone deficiency in thisexample). As noted above, progesterone deficiency symptoms can include(1) anxiety, (2) crankiness, (3) pain or lumpiness in breasts, (4)unexplained weight gain, (5) insomnia, and (6) cyclical headaches. TheGUI 850 can include user input mechanisms such as slider bars 852corresponding to each symptom through which the user quantifies aseverity of each symptom. In this example, the severity is expressed ona percentage scale; but it should be understood that other scales couldbe used (e.g., a score between 1 and 10, a score between 1 and 100, atext score (low, low/medium, medium, medium/high, high), etc.). Also,the user input mechanism need not be slider bars 852 and could be othermechanisms if desired by a practitioner (e.g., radio buttons, data entryfields, etc.). The GUI 850 can also include a “next” button 854 or thelike that is user-selectable to submit the entered symptom experiencedata 314 as well as a “clear” button 856 or the like that isuser-selectable to clear any previously-entered progesterone deficiencysymptom experience data 314.

FIG. 8D shows another example GUI 860 that is designed to solicit andreceive inputs from the patient that quantify additional hormonedeficiency symptoms (e.g., symptoms of an estrogen deficiency in thisexample). The GUI screen 860 may be reached after user selection of the“next” button 854 from FIG. 8C; although it should be understood that apractitioner could switch the navigational order of GUIs 850 and 860 ifdesired or even combine them into a single GUI. Further still, apractitioner may choose to further subdivide the various prompts aboutdifferent progesterone and estrogen deficiency symptoms across more than2 GUIs if desired. As noted above, estrogen deficiency symptoms caninclude (1) vaginal dryness, (2) painful intercourse, (3) hot flashes,(4) night sweats, and (5) lethargy/depression. The GUI 860 can includeuser input mechanisms such as slider bars 862 corresponding to eachsymptom through which the user quantifies a severity of each symptom. Inthis example, the severity is expressed on a percentage scale as withGUI 850; but as noted above other scales could be used if desired by apractitioner. Also, the user input mechanism need not be slider bars 852as noted above. The GUI 860 can also include a “submit” button 864 orthe like that is user-selectable to submit the entered symptomexperience data 314 as well as a “clear” button 866 or the like that isuser-selectable to clear any previously-entered estrogen deficiencysymptom experience data 314 and a “back” button 868 or the like that isuser-selectable to navigate back to GUI 850.

It should be understood that GUIs 850 and 860 are examples only, and themobile application 810 may include additional GUIs if desired by apractitioner. For example, the mobile application 810 may include a“home” GUI that provides navigation to GUIs 850 and/or 860. Other GUIsavailable via mobile application 810 may provide the user with historydata about their symptoms and prescriptions if applicable.

Further still, while FIGS. 8A-8D describe an example embodiment where amobile application is used to collect symptom experience data frompatients, the system can also employ a web application for this purpose.In such a case, the computer system 202 can make a website available foraccess by users via their own computers (e.g., laptops, desktops, etc.).Website pages can then provide the GUIs similar to those shown above forFIGS. 8C and 8D to collect symptom experience data from patients.

Also, while FIGS. 8C and 8D show GUIs for a mobile application 810 foruse by patients, it should also be understood that the system can alsoinclude mobile or web applications for use by other participants insystem 200.

For example, doctors 206 may also have a mobile or web application thatthey can access to review the prescription recommendations generated bythe system 200 for their patients. Such a mobile or web application caninclude a user interface that identifies a patient, the recommendedprescription for that patient, and audit trail data that shows how thesystem arrives at the recommended prescription. An example of such auser interface is shown by FIG. 8E (see GUI 870). FIG. 8F shows anexample process flow with respect to FIG. 8E. At step 890 of FIG. 8F,GUI 870 is presented on a screen of a computer used by the doctor. ThisGUI 870 can be a GUI produced by a mobile app executing on a doctor'smobile computing device (e.g., smart phone or tablet computer), or theGUI 870 can be a web page that is accessed from a website or other webapplication hosted by computer system 202. GUI 870 includes a section872 that identifies the subject patient (which may include any amount ofdesired information that identifies the subject patient). GUI 870 alsoincludes a section 874 that identifies the patient's current biochemicalstatus as derived from assay 210. Section 874 may present not only themeasured hormone levels but also a date for the measurement and a sourcetype for the measurement (e.g., blood, saliva, etc.). GUI 870 alsoincludes a section 876 that identifies the patient's current symptomaticstatus as derived from patient inputs via GUI screens 850 and 860. Thisinformation may present the overall scores for H1DSEF and H2DSEF.However, section 876 may also present additional information such as theindividual symptom severities if desired by a practitioner. GUI 870 alsoincludes a section 878 that identifies the patient's genetic status,which may include identifications of whether any genes that have animpact on treatment options under the system are present in the patient.

At step 892 of FIG. 8F, the system prompts the doctor for approval orrejection of the recommended prescription. This can be accomplished viasections 880 and buttons 882, 884 of the GUI 870. Section 880 of GUI 870displays the recommended hormone therapy treatment prescription. Forease of illustration, section 880 identifies the recommendedprescription in shorthand; however, it should be understood that section880 may provide a more detailed, itemized description of theprescription if desired by a practitioner. GUI 870 permits the doctor toconsider whether the recommended hormone therapy treatment prescriptionin section 880 in the context of the underlying data as presented viasections 874, 876, and 878. If the doctor agrees with the recommendationin 880, the doctor can select the “approve” button 882. Selection of the“approve” button can cause the computer system to place an order for thesubject prescription with a pharmacy (step 894 of FIG. 8F). The subjectpharmacy can be identified based on a data structure that associates thesubject patient with a particular pharmacy or through some othermechanism. If the doctor disagrees with the recommendation in 880, thedoctor can select the “reject” button 884, in which case no prescriptionorder is placed (step 896 of FIG. 8F). As part of step 896, the systemmay also notify the patient about the rejection. Accordingly, it can beseen that GUI 870 provides doctors with actionable intelligence forefficiently prescribing patients with effective hormone therapytreatments.

Also, while the example of FIG. 8E shows a single GUI 870 providing alarge amount of information to the doctor about the patient andrecommended prescription, it should be understood that a practitionermay choose to include additional or less information about the patientif desired.

For example, the GUI 870 may include only the computed H1DF and H2DFvalues in combination with section 880 and buttons 882, 884.

In yet another example, the GUI 870 can present the recommendation inthe context of a visual depiction of the grid data structure 410 wherethe visual depiction also shows which cell the H1DF and H2DF valuesindicate, thereby identifying the recommended prescription.

In another example, the GUI 870 can also include additional sectionsthat present patient medical information such as BMI, medical history,and/or medication history.

In yet another example, the GUI 870 could also include historyinformation for the patient's biochemical and/or symptomatic status. Forexample, section 874 could include plots of the patient's progesteroneand estrogen measurements over time, such as showing measured hormonelevels on a y-axis and time on an x-axis). Such plots could also includegraphical features that show the normal levels for those hormones (forexample, via a horizontal line or bar showing the normal ranges for suchhormone levels). Such a plot can allow a doctor to quickly andintuitively understand if the patient's measured hormone levels areimproving or regressing over time. As another example, section 876 couldinclude plots of the patient's progesterone and estrogen deficiencysymptom experience data over time, such as showing the computed H1DSEFand H2DSEF scores on a y-axis and time on an x-axis). Such a plot canallow a doctor to quickly and intuitively understand if the patient'ssymptoms are improving or regressing over time, thereby allowing thedoctor to assess whether a given treatment may be working or not. Also,such plots can be provided at the individual symptom level if desired bya practitioner.

Further still, a practitioner may choose to spread the subjectinformation over multiple GUIs rather than consolidating on a singleGUI.

Also, GUI 870 can provide the doctor with a mechanism for adjusting theprescription if desirable. For example, the prescription could bedisplayed on the corresponding cell of a visually-presented grid datastructure 410 that was indicated by the H1DF and H2DF scores. If thedoctor decides that a heavier concentration of progesterone and/orestrogen (and/or heavier overall dosage) is desired, the doctor couldselect a nearby cell on the grid data structure 410 to locate aprescription with desirable characteristics, as discussed above andbelow.

Furthermore, in an alternate example embodiment, the computer system 202may not necessarily recommend a prescription to the doctor 206 via thedoctor-facing mobile or web application. Instead, the doctor 206 couldbe provided with a chart or index or the like that corresponds to avisual depiction of data structure 410 (mapping different hormonetherapy prescriptions to particular H1DF,H2DF value pairs), and the GUIpresented to the doctor can show the patient's computed H1DF and H2DFvalues (optionally with supporting audit trail data as well). The doctorcan then consult the chart/index to identify the appropriateprescription for the patient in view of the computed H1DF and H2DFvalues. The chart/index could be provided to doctors 206 offline (e.g.,as a paper chart/index) for reference by the doctors 206 when using thedoctor-facing application; or the chart/index could be electronicallypresented to doctors 206 via one or more GUIs of the doctor-facingapplication.

As yet another example, pharmacies 208 may also have a mobile or webapplication that they can access to receive and review prescriptionorders from computer system 202. Similarly, testing companies thatprocess the assays 210 and 212 could also access a mobile or webapplication for communicating the patient's hormone measurements andgenetic profile data to the computer system 202. Through networkcommunications via a mobile or web application, computers operated bytesting companies can transfer data that represents the patient'shormone measurements and genetic profile via electronic file systemexchanges. However, it should be understood that other techniques fordata transfer could be employed, including manual data entry by anoperator of computer system 202.

Learning and Feedback

Computer system 202 can also provide powerful learning and feedbackcapabilities so that the prescriptions it recommends can improve overtime. Such learning/feedback can be implemented at an individual patentlevel and/or an aggregated multi-patient level. To support suchoperations, the memory 302 within computer system 202 can store historydata about the patients who are using the system 200. This history datacan track the patient's symptoms, hormone measurements, andprescriptions over time (along with any other relevant medicalinformation about the patient). FIG. 9 shows an example of a historydata structure 902 that can be stored in memory 302 in association witha patient (e.g., Patient X) to provide such history tracking. Thehistory data structure 902 can include entries that are tagged by time,where each entry identifies various items of data about the subjectpatient for that subject time. Such data can include the H1 measurement,the H2 measurement, the H1 deficiency symptom experience value(s) (theaggregated HIDSEF value and/or the component values at the individualsymptom level), the H2 deficiency symptom experience value(s) (theaggregated H2DSEF value and/or the component values at the individualsymptom level), the H1DF value, the H2DF value, and the prescriptionselection for the relevant time.

Analysis program 304 can then use this history data to help guide andinfluence future prescription selections for the subject patient. FIG. 9shows an example process flow for this. At step 952, the processoranalyzes the history data in history data structure 902 for the subjectpatient. In an example embodiment, this analysis can be focused on thepatient's symptom data. For example, at step 952, the processor cancompare current symptom experience data with past symptom experiencedata. If this comparison reveals that the patient's symptom experienceshave improved (for symptoms of both H1 and H2 deficiencies), then theprocessor can conclude that the patient's prescription need not bemodified (step 954). However, if this comparison reveals that thepatient's symptoms have not improved, the processor can adjust theprescription selection accordingly.

For example, if the comparison at step 952 shows that the symptomexperience data for neither H1 nor H2 have improved (either they havestayed the same or gotten worse since the last check), then theprocessor can choose to increase the dosage for the current prescriptionformulation (step 956). That is, keep the same clinical mix of H1 and H2in the prescription, but increase the dosage.

If the comparison at step 952 shows that the symptom experience data forH1 has not improved (either it has stayed the same or gotten worse sincethe last check) while the symptom experience data for H2 has improved,then the processor can choose to alter the formulation mix for theprescription so that there is more H1 in the mix relative to theprevious prescription (step 958). With reference to the data structure410 as shown by FIGS. 7D and 7F, this may involve moving to the right inthe grid while staying in the same row until landing on the cell withthe next different formulation. The prescription corresponding to thatcell can then be recommended by the processor.

If the comparison at step 952 shows that the symptom experience data forH2 has not improved (either it has stayed the same or gotten worse sincethe last check) while the symptom experience data for H1 has improved,then the processor can choose to alter the formulation mix for theprescription so that there is more H2 in the mix relative to theprevious prescription (step 960). With reference to the data structure410 as shown by FIGS. 7D and 7F, this may involve moving to the left inthe grid while staying in the same row until landing on the cell withthe next different formulation. The prescription corresponding to thatcell can then be recommended by the processor.

It should be understood that a practitioner can choose to implement sucha learning/feedback model in any of number of ways. For example, thelearning/feedback could be applied for each new prescription and onlyperform a comparison with symptom data from the previous prescription.In another example, the system could combine the symptom data fromseveral prior prescriptions to establish a symptom trend over a longertime duration and then use such an aggregated symptom trend as the pointof comparison rather than just the symptom data from the timeimmediately prior. Also, given that the H1DSEF and H2DSEF values operateto quantify the symptom data for H1 and H2 deficiencies in an aggregatedmanner for each of the H1 deficiency symptoms and the H2 deficiencysymptoms, the comparison at step 952 can be between current/past H1DSEFvalues and current/past H2DSEF values. However, a practitioner couldalso implement such comparisons at a more granular individual symptomlevel if desired. Also, it should be understood that a practitioner candesign the system to analyze more than symptom data at step 952. Forexample, the processor can also compare how the hormone measurementshave changed over time and use that to influence its decision-making asto whether adjustments in the prescription are desirable. For example,if the system detects that a patient has some symptom relief andmeasured hormone levels that are in a medium therapeutic range, thesystem can choose to recommend a decrease in dosage for theprescription. Also, rather than automatically adjusting the recommendedprescription, the system could instead generate a notification to thedoctor 206 regarding which if any of the hormone deficiency symptomsremain problematic for the patient; and the doctor can then use thatinformation to make a decision about increasing dosage and/or changingformulations.

In another example embodiment, the feedback/learning can operate at ahigher level of aggregation across history data for multiple patients.Thus, memory 302 can store a database of history data for multiplepatients, and the processor can analyze such cumulative data to assesswhether any of the hormone therapy formulations and/or any of theassignments of prescriptions to grid cells 702 should be adjusted. Forexample, if a large percentage of patients taking a particularformulation are subsequently changed to a different formulation, thesystem can detect this condition in the history data, and then implementa corrective action. For example, if the history data for a number ofpatients in excess of some defined threshold shows that a particularprescription at a given cell 702 is consistently needing to be changedto a different prescription in order to achieve symptom relief, then thesystem can choose to change the prescription assigned to that grid cell702 to reflect the prescription to which the patients are often beingchanged. Also, if the history data shows a more general problem for aparticular formulation (e.g., Formula X) across multiple cells 702, thesystem may detect this and recommend that Formula X is not particularlyeffective and should itself be adjusted.

In another example embodiment, the history data can be analyzed tooptimize the formulae used by the analysis program to compute H1DF andH2DF. For example, data analysis might reveal that better results can beachieved if different weights were assigned to different symptoms inEquations (2) and (7). Similarly, the weights assigned to the componentsof the genetic metabolism factor may also be adjusted if the historydata shows that these could be further optimized. For example, it may befound that patients taking hormones via the cutaneous skin route do notrequire as much emphasis on liver breakdown enzymes (e.g., CYP450enzymes) and this the increase in dose assigned by the GMF value due togenetic CP450 variants may be over-valued by the system's analysis,which may result in higher measured hormone levels in those patientsthat are detected by follow-up assays 210. In such a situations, apractitioner may choose to modify the weights in the GMF calculation forpatients who are taking hormones via the cutaneous skin route. Furtherstill, data analysis might reveal that better results can be achieved ifdifferent weights and/or scalars were assigned to different componentfactors in Equations (4), (8), (11), (12), (15), (16), (17), and (18).For example, analysis of history data might reveal that better resultswould be achieved if the weight of the symptom data took precedence ofthe weight of the hormone measurement data in the H1DF and H2DFcalculations.

Example Use Case: Progesterone, Estrogen, and Testosterone

While the examples discussed above involved hormone therapy treatmentwith two hormones, it should be understood that treatments using thetechniques described herein could also be extended to additional numbersof hormones, such as a third hormone in the formulation. With respect tothe menopause-related example above involving treatment withprogesterone and estrogen, an additional example embodiment could alsoinclude testosterone as a component of the hormone therapy treatment.Testosterone is well known as a “male” hormone, but women havetestosterone in their blood as well. In fact, testosterone levels inwomen decrease dramatically after menopause like the classic femalehormones estrogen and progesterone. The normal level of testosterone inadult females is 15 to 70 ng/ml; and doctors may choose not to prescribetestosterone (or a testosterone analog or metabolite) to women withtestosterone levels above 25 ng/ml. And, just as estrogen andprogesterone have symptoms associated with low levels (deficiencies),low levels of testosterone in women can also be manifested in symptomssuch as decreased libido, chronic fatigue, painful intercourse, dryskin, and loss of muscle tone in arms and legs. As explained below, thetechnology described herein can also be used to take into account anytestosterone amounts that may be desirable to include in hormone therapytreatment prescriptions.

FIG. 10A shows an example computer system 202 that can generaterecommended hormone therapy prescriptions for mixes of 3 hormones, suchas progesterone, estrogen, and testosterone. The analysis program 1004executed by processor 300 can operate in a similar fashion as theanalysis programs described in connection with FIGS. 3 and 5; but it canalso take into consideration measurement data 1000 for the third hormone(H3) and where the symptom experience data 1002 includes not justsymptom experience data for deficiencies of H1 and H2 but also symptomexperience data for H3 deficiencies.

FIG. 10B shows an example process flow for the analysis program 1004.Steps 600 and 602 can operate as described above. At step 1012, theprocessor computes an H3 deficiency factor (H3DF) based on the subjectpatient's H3 measurement data 1000 and H3 deficiency symptom experiencedata (see 1002). The technique for computing H3DF can be similar to thatdescribed above for H1DF and H2DF. For example, H3DF can be computed asfunction of an H3 Measured Deficiency Factor and an H3 DeficiencySymptom Experience Factor. For example, H3DF can be computed as theproduct of the H3 Measured Deficiency Factor and the H3 DeficiencySymptom Experience Factor, as shown by FIG. 10C and discussed below.

In an example embodiment, the H3 Measured Deficiency Factor (H3MDF) canbe computed according to an inverse relationship with the measured levelof H3 for the subject patient from assay 210. Thus, in an exampleembodiment, H3MDF can be computed as:

$\begin{matrix}{{H\; 3{MDF}} = \frac{1}{\left\lbrack {H\; 3M} \right\rbrack}} & {{Equation}\mspace{14mu} (19)}\end{matrix}$

where H3M represents the measured level of H3 from assay 210. In anexample where H3 is testosterone, the lowest possible score for H3DMF isexpected to be 1/25=0.04 (given that 25 ng/ml can be the treatment limitas noted above); and the highest possible store would be 1/1=1.

In an example embodiment, the H3 Deficiency Symptom Experience Factor(H3DSEF) can be computed as an aggregation of severity data experiencedby the subject patient for different symptoms of an H3 deficiency. Thus,in an example embodiment, H3DSEF can be computed as:

H3DSEF=Σ_(i=1) ^(Q)(H3_(S))_(i)  Equation (20)

where Q represents the total number of different symptoms associatedwith an H3 deficiency, and where (H1_(S))_(i) represents theuser-specified severity value for symptom i associated with an H3deficiency. As noted above, in an example where H3 is testosterone, thesystem may be designed to take into consideration the 5 symptoms oftestosterone deficiency as noted above. Thus, H3DSEF may range in valuefrom 0 (no symptoms reported for all 5 symptoms) to 5 (maximum severityreported for all 5 symptoms). A practitioner may find it desirable topermit the H3DSEF value to zero out if no symptoms are present becausethere may not be a clinical need to treat women with testosterone if notestosterone deficiency symptoms are present (regardless of the measuredtestosterone level).

H3DF can then be computed from H3MDF and H3DSEF as follows:

H3DF=H3MDF×H3DSEF  Equation (21)

Returning to FIG. 10B, once the system has H1DF, H2DF, and H3DF valuesfor the patient, the system can select a recommended prescription fromdata structure 1010. Data structure 1010 can operate in a fashionsimilar to data structure 410, but where data structure 1010 maps thevarious prescriptions to tuples of (H1DF,H2DF,H3DF). In examples whereH3 is testosterone, it should be understood that some of theprescriptions within data structure 1010 may not include anytestosterone (but may include different mixes of progesterone andestrogen).

Furthermore, data structure 1010 may comprise multiple data structures.For example, a first data structure within data structure 1010 could bethe data structure 410 that links H1DF and H2DF values to a particularformulation/dosage of H1 and H2, and a second data structure within datastructure 1010 could be a data structure such as data structure 1020shown by FIG. 10D that links formulations or prescriptions from datastructure 410 with different testosterone dosages. In the example ofFigure OD, where H1,H2,H3 are progesterone, estrogen, and testosterone,Formulations 1-3 can correspond to the different H1/H2 formulations fromFIGS. 7E and 7F, and where different dosages of testosterone can bepaired with each Formulation based on the H3DF score. In the example ofFIG. 10D, this may be implemented as H3DF scores of 0.1 and belowresulting in no testosterone treatment, H3DF scores between 0.11 and0.55 resulting in a low dosage testosterone treatment, and H3DF scoresbetween 0.56 and 1.0 resulting in a high dosage testosterone treatment.The system can establish appropriate amounts of testosterone to be usedas low and high dosages. It should be understood that the testosteronefactor ranges in FIG. 10D are examples; a practitioner may choose tomodify such ranges as he or she deems appropriate.

FIG. 10E shows an example chart that illustrates how such an approachyields 9 unique prescriptions that can be recommended by the system. Inthis example, Formula 1 is progesterone-predominant (progesterone isprovided in a higher clinical dose relative to estrogen) as noted above.Formula 2 provides doses of estrogen and progesterone in equal clinicalstrengths. Formula 3 is estrogen-predominant (estrogen is provided in ahigher clinical concentration than progesterone). The table of FIG. 10Ealso shows relative concentrations of testosterone, which may take theform of DHEA, a preferred testosterone metabolite. Zero, low, or highclinical concentrations of testosterone are combined with Formulas 1-3ultimately yielding 9 different formulations which can meet the clinicalneeds and provide appropriate treatment of the different stages andpresentations of perimenopause menopause for more what is expected to bemore than 95% of women when provided to them in various doses (how muchof the product to use, described subsequently). It should be understoodthat FIG. 10E is merely an example; some practitioners may choose toemploy a greater number of formulations, and or formulations withdifferent mixes of clinical strengths for estrogen, progesterone, andtestosterone.

It should also be understood that data structure 1010 could also beimplemented as a single 3D data structure that maps the variousprescriptions of H1-H3 in three dimensions, where the first, second, andthird dimensions correspond to H1DF, H2DF, and H3DF respectively.

FIG. 10F shows an example GUI 1050 that can be used by mobileapplication 810 to collect symptom experience data from patientsregarding testosterone deficiencies. Slider bars 1052 or other inputmechanisms can be used with each symptom to permit the patient toquantity the severity of any experienced symptoms. Buttons such asbuttons 1054, 1056, and 1058 can be provided to allow the user to,respectively, submit the entered-symptom experience data, clear theentered-symptom experience data, and navigate back to a previous screen.

Also, while the examples discussed above involve hormone therapytreatments with two or more hormones, it should also be understood thatthe technology described herein can be used to recommend or identifyappropriate prescriptions of single hormones if appropriate for a givenpatient. With such an arrangement, the data structure 410 can reduce toa list of prescriptions indexed by a single hormone deficiency value(HDF). HDF can be computed in the same manner as discussed above forH1DF and H2DF. Thus, after the computer system 202 computes an HDF valuefor a patient based on the patient's biochemical, symptomatic, andgenetic status, the computer system 202 can perform a lookup in the datastructure 410 to find the prescription that is linked to the computedHDF value.

Further still, even in the case where the system considers treatment ofthe patient with a mix of two or more hormones (e.g., progesterone andestrogen), it may be the case that for some values of H1DF and H2DF, itis desirable to treat the patient with only one of the multiple hormones(rather than a mix of the two) (e.g., selecting only progesterone orestrogen for treatment). Accordingly, it should be understood that evenwith a grid data structure 410 as shown by FIGS. 7D and 7F, it may bethe case that some of the cells 702 are populated with a prescriptionthat treats the patient with only a single hormone. Thus, while anexample embodiment is discussed above where the data structure 410supports three different prescription formulations of progesterone andestrogen, a practitioner may choose to modify such a data structure tosupport 5 different prescription formulations of progesterone andestrogen (so that prescriptions of only estrogen and only progesteroneare included). Similarly, while an example embodiment is discussed abovewhere the data structure 410 supports 9 different prescriptionformulations of progesterone, estrogen, and testosterone, a practitionermay choose to modify such a data structure to support 11 differentprescription formulations of progesterone, estrogen, and testosterone(so that prescriptions of only estrogen and only progesterone areincluded).

While the invention has been described above in relation to its exampleembodiments, various modifications may be made thereto that still fallwithin the invention's scope. Such modifications to the invention willbe recognizable upon review of the teachings herein.

What is claimed is:
 1. A system for applying computer technology thatsystematically integrates a person's biochemical, symptomatic, andgenetic status to generate a recommended hormone therapy treatmentprescription for the person, the system comprising: a processor; and amemory; wherein the memory is configured to store (1) datarepresentative of a measured level of a first hormone in a person, (2)data representative of a measured level of a second hormone in theperson, (3) symptom experience data for a plurality of symptomsexperienced by the person that relate to a plurality of conditionsassociated with deficiencies of the first and second hormones, and (4)genetic profile data for the person; and wherein the processorconfigured to: access the memory; perform an analysis of the firsthormone measured level data, the second hormone measured level data, thesymptom experience data, and the genetic profile data; and based on theanalysis, generate a recommended hormone therapy prescription to treat ahormone deficiency condition experienced by the person, wherein theprescription comprises (1) a formulation comprising (i) the firsthormone, a biologically active form thereof, an analog thereof, aprecursor thereof, and/or a metabolite thereof, and (ii) the secondhormone, a biologically active form thereof, an analog thereof, aprecursor thereof, and/or a metabolite thereof, and (2) a dosage for theformulation.
 2. The system of claim 1 wherein the processor and memoryare for use in conjunction with a biological sample assay that measures(1) a level of the first hormone in the person and (2) a level of thesecond hormone in the person based on an analysis of a bodily fluidsample from the person, wherein the first hormone measured level dataand the second hormone measured level data are derived from the firstand second hormone levels that are measured by the biological sampleassay.
 3. The system of claim 2 wherein the memory further comprises adata structure that associates a plurality of different prescriptions ofthe formulation with (1) a first factor associated with the firsthormone (2) and a second factor associated with the second hormone; andwherein the analysis translates the first hormone measured level data,the second hormone measured level data, the symptom experience data, andthe genetic profile data into values for the first and second factors;and wherein the processor is further configured to generate therecommended hormone therapy prescription based on a selection from thedata structure of the prescription associated with the translated firstand second factor values.
 4. The system of claim 2 wherein the processoris further configured to provide a user interface for display on adisplay screen, the user interface configured to receive input thatdefines the symptom experience data.
 5. The system of claim 4 whereinthe user interface includes a plurality of slider bars, each slider barcorresponding to a different symptom relating to a condition associatedwith a deficiency of the first and/or second hormone, and wherein theslider bar is adjustable in response to input to define a value within arange of values for severity of its corresponding symptom, and whereinthe symptom experience data comprises the input-defined severity values.6. The system of claim 2 wherein the processor comprises a firstprocessor and a second processor, the first processor being resident ina user computer, the second processor being resident in a server,wherein the first processor is configured to collect the symptom datafrom the user interface, and wherein the second processor is configuredto perform the analysis.
 7. The system of claim 2 wherein the memory isfurther configured to store medical information about the person, andwherein the analysis is also performed on the medical information,wherein the medical information comprises a body fat compositioncharacteristic for the person and/or a surgical history for the person.8. The system of claim 2 wherein the genetic profile data comprises genedata about whether the person has an allele of a gene which affects aperson's metabolism of the first and/or second hormones.
 9. The systemof claim 2 wherein the genetic profile data comprises gene data aboutwhether the person has an allele of a gene linked to an adverse healthrisk for a human if the human is treated with the first and/or secondhormones.
 10. The system of claim 2 wherein the memory is furtherconfigured to store (1) data representative of a measured level of athird hormone in the person obtained from the biological sample assay,and (2) symptom experience data for a plurality of symptoms experiencedby the person that relate to a plurality of conditions associated with adeficiency of the third hormone, and wherein the processor is furtherconfigured to: perform an analysis of the first hormone measured leveldata, the second hormone measured level data, the third hormone measuredlevel data, the symptom experience data for symptoms experienced by theperson that relate to conditions associated with deficiencies of thefirst, second, and third hormones, and the genetic profile data; andbased on the analysis, generate a recommended hormone therapyprescription for the person to treat a hormone deficiency conditionexperienced by the person, wherein the prescription comprises (1) aformulation comprising any combination of (i) the first hormone, abiologically active form thereof, an analog thereof, a precursorthereof, and/or a metabolite thereof, (ii) the second hormone, abiologically active form thereof, an analog thereof, a precursorthereof, and/or a metabolite thereof, and (iii) the third hormone, abiologically active form thereof, an analog thereof, a precursorthereof, and/or a metabolite thereof and (2) a dosage for theformulation.
 11. The system of claim 2 further comprising the biologicalsample assay.
 12. The system of claim 11 wherein the biological sampleassay comprises a plurality of biological assays.
 13. The system ofclaim 2 wherein the processor is further configured to repeat itsoperations for the person over time; wherein the processor and memoryare further configured to (1) track the measured first and secondhormone level data for the person over time, and (2) track the symptomexperience data for the person over time; and wherein the processor isfurther configured to adjust the analysis based on the tracked measuredfirst and second hormone level data and the tracked symptom experiencedata.
 14. The system of claim 2 wherein the processor is furtherconfigured to repeat its operations for a plurality of different personsover time such that the processor and memory (1) track the measuredfirst and second hormone level data for the persons over time, (2) trackthe symptom experience data for the persons over time, and (3) track therecommended prescriptions for the persons over time; wherein theprocessor is further configured to adjust the analysis based on thetracked measured first and second hormone level data and the trackedsymptom experience data for the persons over time in combination withthe tracked recommend prescriptions for the persons over time.
 15. Thesystem of claim 14 wherein the memory further comprises a data structurethat associates a plurality of different prescriptions of theformulation with a first factor associated with the first hormone and asecond factor associated with the second hormone; and wherein theanalysis translates the first hormone measured level data, the secondhormone measured level data, the symptom experience data, and thegenetic profile data into values for the first and second factors; andwherein the processor is further configured to (1) generate therecommended hormone therapy prescription based on a selection from thedata structure of the prescription associated with the translated firstand second factor values, and (2) adjust an assignment of prescriptionsto first and second factors in the data structure based on the trackedmeasured first and second hormone level data and the tracked symptomexperience data for the persons over time in combination with thetracked recommend prescriptions for the persons over time.
 16. Thesystem of claim 15 wherein the processor, as part of the analysis, isfurther configured to compute a measured deficiency value for the firsthormone based on the measured first hormone level data such that themeasured first hormone deficiency value has an inverse relationship withthe measured first hormone level data.
 17. The system of claim 16wherein the symptom experience data comprises first symptom data forsymptoms experienced by the person that relate to conditions associatedwith deficiencies of the first hormone; and wherein the processor, aspart of the analysis, is further configured to (1) compute a symptomaticdeficiency value for the first hormone based on the first symptomexperience data such that the symptomatic deficiency value exhibitslarger values for relatively more severe symptoms for the conditionsassociated with deficiencies of the first hormone and smaller values forless severe symptoms for the conditions associated with deficiencies ofthe first hormone, and (2) compute a deficiency factor for the firsthormone based on a combination of the computed first hormone measureddeficiency value and the computed first hormone symptomatic deficiencyvalue.
 18. The system of claim 17 wherein the processor, as part of theanalysis, is further configured to (1) compute a metabolism adjustmentfactor for the first hormone deficiency factor based on the geneticprofile data for the person, and (2) adjust the first hormone deficiencyfactor based on the computed metabolism adjustment factor.
 19. Thesystem of claim 18 wherein the first hormone comprises progesterone,wherein the second hormone comprises estrogen, wherein the geneticprofile data comprises a presence indicator for the person with respectto allelic status of one or more Cytochrome P450 (CP450) family genes,and wherein the processor is further configured to compute themetabolism adjustment factor for the first hormone deficiency factorsuch that the first hormone deficiency factor will be upwardly adjustedif the genetic profile data indicates the presence of an allele of aCP450 family of genes in the person linked to an increased rate ofsteroid metabolism in a human.
 20. The system of claim 17 wherein thememory is further configured to store a body fat compositioncharacteristic value for the person; and wherein the processor, as partof the analysis, is further configured to (1) compute a body fatcomposition characteristic adjustment factor for the first hormonedeficiency factor based on the body fat composition characteristic valuefor the person, and (2) adjust the first hormone deficiency factor basedon the computed body fat composition characteristic adjustment factor.21. The system of claim 17 wherein the processor, as part of theanalysis, is further configured to (1) compute a risk adjustment factorfor the first hormone deficiency factor based on the genetic profiledata for the person, and (2) adjust the first hormone deficiency factorbased on the computed risk adjustment factor.
 22. The system of claim 21wherein the first hormone comprises progesterone, wherein the secondhormone comprises estrogen, wherein the genetic profile data comprises apresence indicator for the person with respect to allelic status ofBRCA1 and BRCA2 genes, and wherein the processor is further configuredto compute the risk adjustment factor for the first hormone deficiencyfactor such that the first hormone deficiency factor will be zero if thegenetic profile data indicates the presence of either the BRCA1 or BRCA2gene alleles in the person linked to an increased risk of breast cancerassociated with hormone replacement therapy.
 23. The system of claim 2wherein the processor, as part of the analysis, is further configured to(1) compute a value for a first hormone deficiency factor based on themeasured first hormone level data and the symptom experience data, and(2) compute a value for a second hormone deficiency factor based on themeasured second hormone level data and the symptom experience data;wherein the memory further comprises a data structure that associates aplurality of different prescriptions of the formulation with differentvalues for the first and second hormone deficiency factors; and whereinthe processor is further configured to generate the recommended hormonetherapy prescription based on a selection from the data structure of theprescription associated with the computed first and second hormonedeficiency factor values.
 24. The system of claim 23 wherein the datastructure represents a grid of cells, each cell corresponding todifferent prescriptions of the first and second hormones and beingindexed in a coordinate space by a pair of values for the first andsecond hormone deficiency factors, wherein the grid has a first axis anda second axis that define the coordinate space, wherein the first axishas a range of values for the first hormone deficiency factor, andwherein the second axis has a range of values for the second hormonedeficiency factor.
 25. The system of claim 24 wherein the differentprescriptions are a set of X different prescriptions, and wherein thedata structure associates the different members of the X descriptionswith the cells that are deemed clinically appropriate for the personbased on the cell's associated first and second hormone deficiencyfactor values.
 26. The system of claim 25 wherein the first hormonecomprises progesterone, wherein the second hormone comprises estrogen,and wherein X is a value between 3 and
 5. 27. The system of claim 25wherein prescriptions with a higher dose of the first hormone,biologically active form thereof, analog thereof, precursor thereof,and/or metabolite thereof relative to the second hormone, biologicallyactive form thereof, analog thereof, precursor thereof, and/ormetabolite thereof in the formulation are associated with cells of thegrid that are associated with first hormone deficiency factor valuesabove a first threshold and second hormone deficiency factor valuesbelow a second threshold; wherein prescriptions with a higher dose ofthe second hormone, biologically active form thereof, analog thereof,precursor thereof, and/or metabolite thereof relative to the firsthormone, biologically active form thereof, analog thereof, precursorthereof, and/or metabolite thereof in the formulation are associatedwith cells of the grid that are associated with second hormonedeficiency factor values above a third threshold and first hormonedeficiency factor values below a fourth threshold; and whereinprescriptions with doses of the first and second hormone, biologicallyactive forms thereof, analogs thereof, precursors thereof, and/ormetabolites thereof that are of relatively equal clinical strengths inthe formulation are associated with cells of the grid that areassociated with first hormone deficiency factor values between the firstand fourth thresholds and second hormone deficiency factor valuesbetween the second and third thresholds.
 28. The system of claim 25wherein the recommended prescription further comprises a third hormone,biologically active form thereof, analog thereof, precursor thereof,and/or metabolite thereof; wherein the first hormone comprisesprogesterone; wherein the second hormone comprises estrogen; wherein thethird hormone comprises testosterone; and wherein X is a value between 9and
 11. 29. The system of claim 1 further comprising: a plurality ofdifferent formulations of the first and second hormones, biologicallyactive forms thereof, analogs thereof, precursors thereof, and/ormetabolites thereof; and wherein the processor is further configured to,based on the analysis, select a formulation from among the differentformulations for use in the recommend hormone therapy prescription. 30.The system of claim 1 wherein the processor comprises a plurality ofprocessors.
 31. The system of claim 1 wherein the memory comprises aplurality of memories.
 32. A computer program product for applyingcomputer technology that systematically integrates a person'sbiochemical, symptomatic, and genetic status to generate a recommendedhormone therapy treatment prescription for the person, the computerprogram product comprising: a memory configured to store (1) datarepresentative of a measured level of a first hormone in a person, (2)data representative of a measured level of a second hormone in theperson, (3) symptom experience data for a plurality of symptomsexperienced by person user that relate to a plurality of conditionsassociated with deficiencies of the first and second hormones, and (4)genetic profile data for the person; and a plurality ofprocessor-executable instructions that are resident on a non-transitorycomputer-readable storage medium, wherein the instructions areconfigured for execution to cause a processor to: access the memory;perform an analysis of the first hormone measured level data, the secondhormone measured level data, the symptom experience data, and thegenetic profile data; and based on the analysis, generate a recommendedhormone therapy prescription to treat a hormone deficiency conditionexperienced by the person, wherein the prescription comprises (1) aformulation comprising (i) the first hormone, a biologically active formthereof, an analog thereof, a precursor thereof, and/or a metabolitethereof, and (ii) the second hormone, a biologically active formthereof, an analog thereof, a precursor thereof, and/or a metabolitethereof, and (2) a dosage for the formulation.
 33. A method forgenerating a recommended hormone therapy treatment prescription based ona systematic analysis of a person's biochemical, symptomatic, andgenetic status, the method comprising: storing data in memory thatrepresents a measured progesterone level and a measured estrogen levelin a female person, wherein the stored data representing the measuredprogesterone and estrogen levels are derived from a biological sampleassay with respect to a bodily fluid sample from the female person;storing data in memory that represents experiences for the female personwith respect to a plurality of symptoms that relate to a plurality ofconditions associated with deficiencies of progesterone and/or estrogenin a human female; storing data in memory that represents a geneticprofile for the female person; storing data in memory that represents abody fat composition characteristic for the female person; storing adata structure in memory that associates a plurality of differenthormone therapy treatment prescriptions with different progesteronedeficiency factor values and different estrogen deficiency factorvalues, each prescription comprising a formulation and dosage ofprogesterone and estrogen, biologically active forms thereof, analogsthereof, precursors thereof, and/or metabolites thereof, a processorprocessing the measured progesterone level data, the measured estrogenlevel data, the symptoms experience data, the genetic profile data, andthe body fat composition characteristic data according to a computerprogram that computes a progesterone deficiency factor value and anestrogen deficiency factor value as a function of the measuredprogesterone level data, the measured estrogen level data, the symptomsexperience data, the genetic profile data, and the body fat compositioncharacteristic data; and a processor selecting the prescription that isassociated by the data structure with the computed progesteronedeficiency factor value and the computed estrogen deficiency factorvalue, wherein the selected prescription serves as the recommendedhormone therapy treatment prescription for the female person.
 34. Themethod of claim 33 wherein the symptoms experience data comprises (1)progesterone deficiency symptoms experience data that quantifies aseverity of a plurality of symptoms experienced by the female personthat relate to a plurality of conditions associated with deficiencies ofprogesterone in a human female, and (2) estrogen deficiency symptomsexperience data that quantifies a severity of a plurality of symptomsexperienced by the female person that relate to a plurality ofconditions associated with deficiencies of estrogen in a human female,and wherein the processing step comprises: a processor computing aprogesterone deficiency value according to a formula where the computedprogesterone deficiency value has an inverse relationship with themeasured progesterone level; a processor computing a symptomaticprogesterone deficiency value based on the progesterone deficiencysymptoms experience data such that the symptomatic progesteronedeficiency value exhibits larger values for relatively more severesymptoms for the conditions associated with progesterone deficienciesand smaller values for less severe symptoms for the conditionsassociated with progesterone deficiencies; a processor scaling thecomputed progesterone deficiency value based on the computed symptomaticprogesterone deficiency value; a processor computing an estrogendeficiency value according to a formula where the measured estrogenlevel has an inverse relationship with the computed estrogen deficiencyvalue; a processor computing a symptomatic estrogen deficiency valuebased on the estrogen deficiency symptoms experience data such that thesymptomatic estrogen deficiency value exhibits larger values forrelatively more severe symptoms for the conditions associated withestrogen deficiencies and smaller values for less severe symptoms forthe conditions associated with estrogen deficiencies; a processorscaling the computed estrogen deficiency value based on the computedsymptomatic estrogen deficiency value; a processor computing ametabolism adjustment factor based on the genetic profile data; aprocessor computing a body fat composition characteristic adjustmentfactor based on the body fat composition characteristic data; aprocessor computing the progesterone deficiency factor value byadjusting the scaled progesterone deficiency value based on the computedmetabolism adjustment factor and the computed body fat compositioncharacteristic adjustment factor; and a processor computing the estrogendeficiency factor value by adjusting the scaled estrogen deficiencyvalue based on the computed metabolism adjustment factor and thecomputed body fat composition characteristic adjustment factor.